{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as sio    # 用于导入mat文件\n",
    "import seaborn as sns     # 用于绘制散点图\n",
    "import scipy.stats as stats   # 用于绘制高斯分布图\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"white\")\n",
    "%matplotlib inline\n",
    "plt.style.use({'figure.figsize':(8, 8)})\n",
    "#sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = 'heightWeight.mat'\n",
    "raw_data = sio.loadmat(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', 'heightWeightData', '__globals__'])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.keys()  # 查看数据的键"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(raw_data['heightWeightData'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,  67, 125],\n",
       "       [  2,  68, 140],\n",
       "       [  2,  67, 142],\n",
       "       [  2,  60, 110],\n",
       "       [  2,  64,  97]], dtype=uint16)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对数据进行初步了解\n",
    "data = raw_data['heightWeightData']\n",
    "data[:5,:]  # 第1列：1表示男性，2代表女性；第2列：体重；第3列：身高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x13fc3898>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432.85x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将numpy数组转换为pandas数据结构，以使用seaborn绘图\n",
    "columns = ['sex','height','Weight']\n",
    "df = pd.DataFrame(data=data,columns=columns)\n",
    "df['sex']=df['sex'].astype('category')    # 将sex这一列转换为类别类数据\n",
    "df['sex'].cat.categories=['male','female']  # 将1,2转换为相应的类别\n",
    "sns.relplot(x='height',y='Weight',hue='sex',style='sex',data=df)   # 利用seaborn进行绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#fg,ax = plt.subplots(111)\n",
    "# 将属于男性的数据提取出来\n",
    "df_male = df[df['sex']=='male']\n",
    "# 将属于女性的数据提取出来\n",
    "df_female = df[df['sex']=='female']\n",
    "male_cov,female_cov = df_male.cov(),df_female.cov()  \n",
    "male_mean,female_mean = df_male.mean(),df_female.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>9.783866</td>\n",
       "      <td>47.589041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weight</th>\n",
       "      <td>47.589041</td>\n",
       "      <td>1049.656393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           height       Weight\n",
       "height   9.783866    47.589041\n",
       "Weight  47.589041  1049.656393"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_cov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "rv = stats.multivariate_normal.rvs(mean=male_mean,cov=male_cov,size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对男性数据的协方差矩阵进行特征分解\n",
    "eigvals,U = np.linalg.eig(male_cov)   # 矩阵U表示由特征向量组成的正交矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   7.61052906,    0.        ],\n",
       "       [   0.        , 1051.8297297 ]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D=np.diag(eigvals)  # 由特征值构造出来的对角矩阵D\n",
    "D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = np.power(stats.chi2.ppf(0.95,2),0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "t = np.linspace(0,2*np.pi,200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 200)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xy=np.array([np.cos(t),np.sin(t)])\n",
    "xy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 200)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w=np.dot(np.dot(k*U,np.power(D,0.5)),xy)\n",
    "w.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "height     71.657534\n",
       "Weight    175.616438\n",
       "dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-6.74561423, -6.85658249],\n",
       "       [ 0.30806448, -2.19555352]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w[:,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=w+male_mean[:,np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80, 280)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax=plt.subplot(111)\n",
    "ax.plot(z[0,:],z[1,:])\n",
    "ax.set_xlim([55,80])\n",
    "ax.set_ylim([80,280])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "[[  1  67 125]\n",
      " [  2  68 140]\n",
      " [  2  67 142]\n",
      " [  2  60 110]\n",
      " [  2  64  97]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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dsK9hQLcu0ZzXM5bzesbSr1dnEs+KwTAMXx9uwFExKCIiIiIibebA4XI2/lBE1q5DZO05RHFZNQChTgfndmvPBX0T6BEfQ4/4aM7qHEmXDhFEhjubXLyZpklldR0HDlew76CLvINH2bOvlO++P8jn3+YB0KVjBINT4xnWvysDU+Jwhmh23MmoGBQRERERkRbzeEyyc4tZl1XAt9mF5B90AdCpfTj9k+JIS+rMeWfHcnZCTJsUZYZhEBkeSq/uHejVvUPDdtM0KSyuYPPOIjK/P8iXG/exfN1e2keFcfHAblw57BzO7dbhNK9sPyoGRURERESkWUzTZMfeI3y5aR9fb95HcVk1zhCDfr26MGbYOQxOjad7XLRPh2oahkFC5ygSOkcx+sJzqK1z8933B/ly0z5WbPiRjDW5pPaM5dfDk7hoQDdCHBpGqmJQRERERESa5HBpJZ9/m8eKb35kX1E5oU4HQ/qcxSUDuzGkz1lEhodaHWKDUGcIQ/t1ZWi/rrgqaljxbR7//DqHee98S/e4aK4fmcIvByfauihUMSgiIiIiIidwV1bywwsvk/LA/WTlu1i6Oodvth/AY0Jar85cd3lvLhrQ1a8KwFOJjgzjmuFJXH1JL9ZtLeC9z3bwwt838uGq3dwxLo1B58VbHaIlVAyKiIiIiMgJirZsY2VuNc8/u5KCsjraR4Ux4fIUfnXB2XTrEm11eC0S4jC4eGA3LhrQla+37OevS7cz+821DE1L4J5rB9ClY4TVIfqUikEREREfe+WVV8jIyABgxIgRTJ8+nZkzZ5KZmUlExLEfIvfffz9XXHEFa9as4ZlnnqG6uporr7yShx56yMrQRcQGjlbU8PHXOXz4yY+URw/g7OoKHrrxQi4Z2C1o1vMzDINLBnZnaFoCH325h799soN/m/c5t1/dlzHDzrHNshQqBkXE9uqHwfR+cAohEfa6Iii+t2bNGlavXs3ixYsxDIM777yTTz/9lKysLN59913i438aqlRVVcVjjz3GO++8Q9euXZk8eTKrVq1ixIgRFh6BiASrvA2bWLI6ly8KoNpjkOIu5hJzH2dXFJHwYygHf8wEIDo5iY4D+lscbdsIdYYw4fIULh7Yjf/6x2ZefX8L32QX8sBvB9Ehup3V4XmdFtwQEdsr257N0R07KMv+3upQxAbi4uJ49NFHCQsLIzQ0lKSkJPbv38/+/ft57LHHGDduHC+99BIej4ctW7bQs2dPEhMTcTqdjBs3jmXLlll9CCISZFwVNbyTkc3D7//I8nxIKs9nsms1N1dvpldIOQYGBRnL2ff+YgqXf4Lp8VgdcptL6BzFU5OHcfdv+rNxRxF/eG4l2/Yctjosr/PqnUENgxGRQHB4/QbMOjeH160n9vxBVocjQS4lJaXh33Nzc8nIyGDBggVs2LCBJ598kpiYGCZPnsyiRYuIjIwkLi6u4fnx8fEUFhZaEbaIBKGaWjcff53Dws9+wFVZy8UDuzHh/M7U/GMLVQfKcYRHAeAID4eKciJTkki6527CExIsjtw7DMNg3KW9SOvVmf/8f98w6/WvuXfCQEYN7Wl1aF7jtWJQw2BExF+VbNmKa9fuhselW7MI7dSJ0q1Z5H/wYcP2YBoGI/5n586dTJ48menTp9OrVy/+67/+q+Fvt956Kx9++CGjR49uNG/FNE3bzGMREe8xTZNVG/fxzj+3c/BIJeenxjNpbN+GBdnrek1j80NTMd1uqouKCIuLAwxSZ0yzxXSKXt078NwDw/nPd77l5YWbyCs8yu1Xp+EIwiUovFYMHj8MBjhhGExhYSFXXHEF999/f6NhMEDDMBgVgyLiDabbTeHyT6hzleOIjMThdOJwOnHX1lKQsRxPRQXO6Ciiek22OlQJUpmZmfzhD3/gscceY+zYsezYsYPc3FxGjx4NHPuh5nQ6SUhIoKioqGG/oqKiRhdTRUSaa1d+CW8u3kp2bjG9undgysR00ns3/l4pz9kDDoPaslLcVZXUlZYSEhGBa08OHdL6WhS5b0VHhvHvd17In5Zk8eGq3ZSV1/CHiemEhATXLDuvFYMaBiMi/ip2UDppc2az+7U3qTpw4NjwF+wzDEasVVBQwH333cfzzz/PsGHDgGPF33/8x39w4YUXEhkZyXvvvcf48eMZOHAgOTk57N27lx49erB06VImTJhg8RGISCAqK6/h//1zO5+s30v7qDD+MDGdkb84+6R3u4rXf0vdURchUZGYEZE4wttR53JRvOEb2xSDACEhDiaP70+H6Hb8bfn3VFbXMf3WITiDqCD0ejdRDYMREX8UnpDAeTOODYPBNMEwjv2vjYbBiDXeeustqqurmTt3bsO2G264gbvvvpsbb7yRuro6Ro0axdVXXw3A3LlzmTJlCtXV1YwYMYIxY8ZYFbqIBCDTNPniu3z+e0kWrspaxl3aixtHpRIdceJC8fXTKEo2bqJ9Wl8qfvyRsC5dMGtraJ/Wl5KNm8jv0MFW0ygMw+DGUecRFeHkTx9m8dyCTKbePDho7hB6tRjUMBgR8Wf1w2Dqyl3UlZTi7NABh9Npq2Ew4nuzZs1i1qxZJ/3bzTfffMK2YcOG8dFHH3k7LBEJQgWHynn1/c1s+qGI83rGct91AxvmBZ7M8dMo6qqqGk2jcO3JwVNRQeHyT4JqGkVTl5f69aVJuN0mf/7fbYSFhvDgDYOC4uaV10ra+mEw8+fPZ+zYscBPw2BKS0upra3lvffe44orrmg0DMbtdrN06VKGDx/urdBERICfhsGEx8XhCG9HeHxcwzAYERGRQOXxmHz05W7un7+SH348wj3XDuA/77/0tIUg/DSNIjo5CYfDaDSNwuEwiE5JIm3ObGLTB/riMHyiOctLjf9lMjeNTuXzb/NYsCw4lqPy2p1BDYMREX9XXVREz1tuwrV7NxU/5hHRvRtxwy+lZMsWq0MTERFpkf2HXLz03ia27TnMkD5ncf/1A+ncoelTH+w2jaK5y0vdcEVvDpVU8t5nPxDfKTLgl53wWjGoYTAi4q/q50REpyRTV1FBada2Y0tLZG0jvGtXopOTyf/gQ1vNiRARkcBmmibL1uby1v9uw+kweOC3gxj5i8QWDWWsn0bhrqwAtxscIRgOR1BMo2jt8lKGYXDvhAEUHangtfc3c/ZZMaSe08knsXuD1xvIiIj4GzsuLdHUOREiIhJ4Sl3VvPTeJjZsP0B67zge+O0gunRs+Xd9/TSKjukDSJx4HXkLF1GyaUtQdBNti98AzhAH024dwkPPr+KZt7/hhYdHEBsT7sOjaDvB0QZHRKQZNCdCRESCxcYdB5kyfyXf7TjIndf0Y85dw1pVCMJP0yh6P/wgkWefTe+HH6TnLTdRfVzDx0DVVr8BYiLDeGzSBbgqanhuQSYej+mL8NucikERsaX6ORFQPxeCRnMigm2NwePnRIiISOBzuz28k5HNk39aS0xUGH98cDjXDE866bqBzZU6YyoJY0ZhOI6VCobDQcKYUaROn9rq1/YHbfUboFf3Dtz1m/5s3nmIj77a47V4vUnDREXEtjQn4tRzIkRExH8dLq3k2Xcz2bbnMKOG9uSu3/QjPEw/65ujrX4DjL6wJ99mF/L2x9tJ7x3HOV3bezHqtqc7gyJiW/VzImJSkuk7+3FieicHzdIS9XMi9r2/mIKM5YCBw+kEDAoylrPv/cUULv8E0+OxOlQREWmGrbsP8cAfv2B3fgkP33Q+UyamqxBsgbb6DWAYBlMmphMdEcpL723EHWDDRVUMiohtaU5EcM2LFBEJZqZp8vHXOTzx+hpiIsP444MjuGxwotVhBay2/A3QIbodd/2mHzvzSli6OrCGi+oygojYVuqMxnMf6udEJIwZZVFEbctua0WJiASr2jo3r3+wlU/W72VIn7OYevNgoiJCz7jfoZJKVmbm8dWmfZRX1REV7uTS9O5cNjix1U1mvMGXna/b+jfApendWZmZz7sZ2VzUvxtxsf53fk9GdwZFRILY8XMi6spKcVdUgGHg2pNjdWgiItIER8qqePy1NXyyfi/Xj0xh1h1Dm1QIZucUM/uNY/uZHpOYCCemx+ST9XuZ/cYasnOKfRB98wRy52vDMLjn2gG4PSb/75/brQ6nyVQMiogEsWCeFykiEux25h3hoRdWsWd/KdNvHcJtV/UlpAndQg+VVPLywo0AREeE4nQ6MAwDp9NB9L8KyZcXbuRQSaVX42+uQO98fVanSMb/Mpkvvsvn+73+V2yfjIaJiogEsfo5EWeN+hWGw0Hvhx+k8JPPKNmyxerQRETkNL7IzOOlhZuIjWnHvPsvpVf3Dk3ed2VmHrVuT0Ph93PtwkJwVdayMjOP60f2bquQmy0YO19fd3kKn67fy58/2sZ/3n8JhtH6pT68ScWgiEgQC/Z5kSIiwcY0TRZ+9gPvLvuefkmdefS2X9Ahul2zXuOrTfsIDw057XPCQ0P4atM+S4vB+s7Xda5yHJGROJxOHE4n7tpaCjKW46mowBkdRVSvyZbF2FwR7ZzcODqVVxdtJvP7gwzpc5bVIZ2WikERCXiBNkEefDtJXkREAoPbY/LG4i1krMnll4N78IeJgwh1Nn9WV3lVHTERp/+ZHxJi4Kqsa2mobaK+8/Xu196k6sCBRp2vqSgnMiWJpHvubvIi8P7iV784m/c/38mCZdkMTo3367uDmjMoIgEtECfIQ2BPkhcRkbZXXetm7tsbyFiTy4TLknnohvNbVAgCRIU7cbtPv96d220SGW79faH6ztdQ3/GaRp2vA60QBAh1OrjhivPYlV/KN9sLrQ7ntFQMikjACtQJ8hD4k+RFRKTtHK2o4YnX17B+2wHu/k1/Jl2dhqMJjWJO5dL07lTVuk/7nKpaN5emd2/xe7SlYOx8/cvBPYiPjeD9lTutDuW0rL8cICLSQoEyQR6Cc5K8iIi03sEjFfz7n9ZScKiC6bcO4ZKBrS/QLhucyMpv86iucdMu7MS5g9U1bkJDHH6zaH195+uO6QNInHgdeQsXUbJpC8UbvqFDWl+rw2sRZ4iDa4Yn8aclWWTnFNPn3E5Wh3RSujMoIgGrORPkrVY/SX7f+4spyFgOGDicTsCgIGM5+95fTOHyTzA9HqtDFRERH8ktKGPaS19RXFrFU5OHtUkhCNClYwRTJg4CwFVZS12dB9M0qavz4KqsBWDKxEF+M6++vvN174cfJPLss+n98IP0vOUmqouKrA6tVa4Y2pPoiFAWr9pldSinpDuDIhKwAmWCPATvJHkREWmZnXlHmP3GWtqFhTD3/ks5p2v7Nn39Pud24qnJFzU0WHNV1hEZ7mTU0J5+12AtWDtfR7Q7dr4//HI3h0sr6dzBf855PRWDIhKw6ifIO52nnlfhLxPk4adJ8psfmnpscrxhNJokr66iIiL2sGNvMU++uZaoyDD+496LOatTpFfep0vHCK4f2dvyqRJ2NmbYOSxetYvl6/Zy0+hUq8M5gYaJikjACrQJ8hCck+RFRKTptucc5ok31tI+qh1z/+0SrxWC4h+6doli0HnxLF+3F7fb/6aCqBgUkYB12eBEQkMcVNecvCD0twny8NMk+ZiUZPrOfpyY3snUuVwUb/jG6tBERMTLtu4+xJNvrqVT+3Y8c9/FxMVqRIgdjB7ak+KyKjbvPGR1KCdQMSgiASvQJshD8E6SFxGR09v8QxH//qd1xMVG8My/XeKX88fEO37R9yyiI0L5/Ns8q0M5gX9MpBERaaFAmiAPwTtJXkRETu277w/yf/+ynq5donj6novpGNPO6pDEh0KdIVya3p0V3+ZRUVVLZPjJl8SygopBEQl4miAvIiL+KvP7Qp7+8wbOPiuGpyYPo0O0CkE7+uXgHmSszWXD9kJ+eX4Pq8NpoGGiIiIiIiJesG3PYf7jr99w9lkxPH3vRSoEbSy1Zyc6tW/Hmi37rQ6lERWDIiIiIiJtbFdeCXP+ex1xHSOYc/cwYiLDrA5JLORwGFzYryuZ3x+kqtr69Y/rqRgUEREREWlDeYVHefJPa4mJDOXpey7SHEEB4KIB3aipdbPxh4NWh9JAxaCIiIiISBspLK7giTfW4HAY/J/JF/ldIzOxTlqvzkSGO8n8XsWgiIiIiEhQKS6r4onX11Bd4+YYaybxAAAgAElEQVT/TL6IbnHRVockfsQZ4mBgShyZ3x/ENE2rwwFUDIqIiIiItNrRihpmv7GGI0erePKuCzmna3urQxI/NDg1nkMllfxYeNTqUAAVgyIiIiIirVJVXcecP61jX1E5s24fSmrPTlaHJH5q0HnxAGzeWWRxJMeoGBQRERERaSG3x2T+gkx25h1h+q1DGNg7zuqQxI/Fx0YS3ymSrN2HrQ4FUDEoIiIiItJif/4oi/XbDnDXb/ozrH9Xq8ORANA/qTNZuw/j8Vg/b1DFoIiIiIhIC3z01W4++moPvx7ei6sv6WV1OBIg+vXqwtGKGvIOWj9vUMWgiIiIiEgzrc8q4L+XZHFhvwTuGNfP6nAkgPQ599ic0h17j1gciYpBEREREZFm2Zl3hGcXZJLcoyOP3DyYEIdhdUgSQLp2jiIqIpQfflQxKCIiIiISMA4WV/DUW+vpEN2OJ34/lPAwp9UhSYBxOAx6J3Zk548lVoeiYlBEREREpClclbXMeWsdtbVunvz9UGJjwq0OKai4KyvJfmYe7spKq0PxupSzY8k9UEZ1rdvSOFQMioiIiIicgdtjMv/db9l30MXMSRdwdoIWlW9rZduzObpjB2XZ31sditf16tYBj8ckz+LF51UMioiI+Ngrr7zC2LFjGTt2LPPmzQPgvffe4+qrr2bcuHHMnDmTmpqahudedtllXHPNNVxzzTUsWLDAytBFbGvBsmwyvz/I5GsHMDBFawl6w+H1GzDr3Bxet97qULyuZ9cYAHL3l1kahwY5i4iI+NCaNWtYvXo1ixcvxjAM7rzzTt58800WLVrEBx98QFRUFI8++ih/+9vfmDRpEllZWfzxj39k0KBBVocuYltfb97PP1bsZPSFPbly2DlWhxM0SrZsxbVrd8Pj0q1ZhHbqROnWLPI/+LBhe3RyEh0H9LciRK/p2iWaMKeD3AIVgyIiIrYRFxfHo48+SlhYGABJSUnU1NTw5JNPEh0dDUDv3r3Zv38/AFlZWbzxxhvs27ePX/ziF8yYMYN27dpZFr+I3ewtKOOFv3/HeT1jmTw+uAoSq5luN4XLP6HOVY4jMhKH04nD6cRdW0tBxnI8FRU4o6OI6jXZ6lDbXIjD4OyEGPYesLYY1DBRERERH0pJSSE9PR2A3NxcMjIyuPrqq7n44osBKC4uZsGCBYwcOZLy8nL69OnDtGnTWLx4MWVlZbz66qtWhi9iK66KGv7vXzYQ0c7JzN/9glBniNUhBZXYQemkzZlNdHISDoeBI/xYQx5HeDgOh0F0ShJpc2YTmz7Q4ki9o1tcNPuLXJbGoGJQRMQCduqYJie3c+dO7rjjDqZPn84555wDQGFhIb/73e+YMGECQ4cOJSoqij/96U8kJSXhdDq54447WLVqlbWBi9iE22Py7IJMikoqmPm7C+jcIcLqkIJSeEIC582YBhhgmsc2miZgkDpjGuEJCVaG51Xd46IpKqmkxsKOoioGRUQsYKeOaXKizMxMJk2axCOPPML48eMB2L17NzfccAPjx4/nvvvuA2D//v0sWrSoYT/TNHE6NcNDxBcWLMvmu+8Pcvf4AfQ5t5PV4QS18pw94DBwV1ZQV1aKu6ICDAPXnhyrQ/OqbnHRmCYUHC63LAavFoPqliYicnJ26pgmjRUUFHDfffcxf/58xo4dC4DL5eL3v/89DzzwAHfccUfDc8PDw3n22WfJy8vDNE0WLFjAFVdcYVXoIrahhjG+Vbz+W+qOuohJSabv7MeJ6Z1MnctF8YZvrA7Nq7p1iQKg4JB1xaDXLi+qW5qIyE/s3DFNGnvrrbeorq5m7ty5DduuuuoqDh06xF/+8hf+8pe/AHD55ZfzwAMP8NRTT3HvvfdSW1vL+eefz+23325V6CK2sL/IxYvvqWGML1UXFdHzlps4a9SvMBwOej/8IIWffEbJli1Wh+ZV8bGRABQdsW7KiNeKQXVLExH5iZ07pkljs2bNYtasWSdsnzz55J/96NGjGT16tLfDEhGgts7NvHe/JcThYPqtQ9QwxkdSZ0xt9NhwOEgYM4qEMaMsisg3OkSHEep0cKjEumLQa8NE1S1NROQndu+YJiISCP6ydDu780t58IZBDXdtRLzFMAziOkZQFIzFYD11SxMROcbOHdNERPzd2q0F/O9Xe/j18F4M7dfV6nDEJrp0jAjOO4OgbmkiIj9n145pIiL+7GBxBS++t5HkHh2YNLav1eGIjXSMaUfJ0WrL3t9rxaC6pYmInMiuHdNERPxVndvDs+9+i8djMv1WLSwvvtUxuh0lLuuKQa/dflO3NBGRE9m1Y5qIiL96NyOb7/ceYfqtQ+j6r1b/Ir7SMaYdldV1VNe6aRfq+wsRXisG1S1NROREdu2YJiLij777/iDvr9zF6At7cml6d6vDERvqEH1s9YTSo9XEd/J90yKvN5AREREREfE3pa5qnv+f7+iZEMNdv9F6gmKN6IhQAMqrai15fxWDIiIiImIrpmnyX4s246qsZdotQywZnicCEPWvYtBVqWJQRERERMTrVmbms3ZrAbde2YeeXdtbHY7YWH0xWKFiUERERETEu4qOVPLG4i2k9erMNSOSrA7HJ9yVlWQ/Mw93pXXr2cnJaZioiIiIiIgPeDwmL773HaZp8uANgwhxGFaH5BNl27M5umMHZdnfWx2K/Eyo81g5VlPrseT9VQyKiIiIiC18/HUOm3ce4ve/7kdCZ/ssI3F4/QbMOjeH1623OhT5GWfIsXKszm1NMei1pSVERERERPxFXuFR/rp0G0P6nMWooT2tDserSrZsxbVrd8Pj0q1ZhHbqROnWLPI/+LBhe3RyEh0HqJOqlervDKoYFBERERHxArfbw/P/8x3twkKYMjEdwwju4aGm203h8k+oc5XjiIzE4XTicDpx19ZSkLEcT0UFzugoonqdfP1v8Z2QhjuDpiXvr2GiIiIiIhLUFn2+k515JfzbdQPp1D7c6nC8LnZQOmlzZhOdnITDYeAIP3bMjvBwHA6D6JQk0ubMJjZ9oMWRitXDRFUMioiIiEjQ+vFAGX//dAfD07tzycDuVofjM+EJCZw3YxpggPmvu06mCRikzphGeEKCleHJv4Q4DBwG1NWpGBQRERERaTMej8kr/9hMRDsnd/3GfnPjynP2gMPAXVlBXVkp7ooKMAxce3KsDk2OExLi0J1BEZHW0jpKIiJyvGXrcsnOLeb3v+5Hx5h2Vofjc8Xrv6XuqIuYlGT6zn6cmN7J1LlcFG/4xurQ5DjOEIdlcwbVQEZEgsbx6yjFnj/I6nBERMRCh0sr+evS7aSnxHH5kESrw7FEdVERPW+5ibNG/QrD4aD3ww9S+MlnlGzZYnVocpwQh4FbdwZFRFpH6yiJiEi9NxZvxe0x+bfrBgZ999BTSZ0xlYQxo/BUV5P9zDw81dUkjBlF6vSpVodmWycbxVTn9uB0WlOW6c6giAQsraMkIiIns3brftZuLWDS2L507WKfxeVPRSNn/MfJPovaOk/DeoO+pmJQRAKW1lESEZGfK6+s5fUPttCrWwd+MyLJ6nD8wvEjZ1QMWuvnn4XHY+L2mISGqBgUEWmW+nWUdr/2JlUHDjRaR4mKciJTkki65261zxYRsZG3P95OydFqnrjjwoYFve1GI2f8x5k+ixo3gEHdoSIg1efxqRgUkYBWv47S5oemHls/yTAaraMUEhFhdYgiIuIj23MOk7E2l9+MSCI5saPV4VhGI2f8x5k+i4rKGjjr14QY1nQTteflEhEJKlpHSURE3B6T1z/YQpeOEdw82vd3WPxJ/ciZ6OQkHA6j0cgZh8MgOiWJtDmziU0faHGkwe9Mn0V4r2NDmTuc3cOS+FQMikjA0zpKIiKybG0uOfvLuPPX/Qhvp8Fv9SNnoH7EDI1GzmgKhe+c7rPoee+9ADidIZbEpv9SRCTgaR0lERF7Kyuv4d2MbAYkd+GiAV2tDsdvHD9yBrcbHCEYDgeuPTl0SOtrdXi2cqrPojR3L4Bl3UR1Z1BEAp7WUfJPJ1tLSUTEG97NyKaiuo67x/e37ZqCJ6ORM/7jVJ/FoU1bAeuKQd0ZFJGgoXWU/Is+DxHxhd35JSxbl8u4S3rRM6G91eH4FY2c8R+n+iy++/YHoD0xkaGWxKU7gyISNI5fu0esp89DRLzNNE3eWLyV9lFh3GjzpjEnUz9yxnAc+8lvOBzNGjmjER5t51SfRew11wIQExlmSVy6MygiAUvrKPkXfR4i4mtffJdPdm4xf5iYTnSENXdWgplGeHhfWXktADFRKgZFRJpF6yj5F30eIuJLFVW1/HXpNlISOzLyF2dbHU5QOn6Eh4pB73BV1AC6Mygi0mz1a/fsfu1Nqg4caLR2DxXlRKYkkXTP3Wqf7SP6PETElxZ+9gPFZdU8NukCHA41jWkLGuHhe0cranCGOAgPs2ZpCc0ZFJGApnWU/Is+DxHxhcLiCpZ8uYeRv0jkvJ6drA4naNSP8Nj3/mIKMpYDBg6nEzAoyFjOvvcXU7j8E0yPx+pQg0ZZeQ0xkaGWdcFVMSgiAe/4tXvqykpxV1SAYeDak2N1aLakz0NEvO3djGwcDoNbxvSxOpSgUj/CIzo5CYfDaDTCw+EwiE5JIm3ObGLTB1ocafBwVdZaNl8QVAyKSBDQOkr+RZ+HiHjTrvwSvvgun2uG96JLxwirwwk6GuHhW8fuDFpXDGrOoIgEPK2j5F/0eYiIN729dDsxkWFMuCzF6lCC1vEjPHC7wRGC4XDg2pNDh7S+VocXVI5W1NCtS5Rl769iUEQCXuqMxusl1a/dkzBmlEUR2Zs+DxHxlu92HGTTziLuuqYfUVpKwmvqR3h0TB9A4sTryFu4iJJNWyje8I2KwTZ2uKSSAUldLHt/DRMVEdvToroiIv7P4zH569JtnNUpkisvOsfqcIJa/QiP3g8/SOTZZ9P74QfpectNVBcVWR1aUKmoqqW8qs7S4c4qBkXE9o5fVFdERPzTF9/lk7O/jNuu6kOo05o2/HaROmMqCWNGYTiOlQr1IzxSp089w56tY7eLs0Ulx44zLlbFoIiIZY5fVFfEF1555RXGjh3L2LFjmTdvHgBr1qxh3LhxjBo1iueff77hudnZ2Vx77bWMHj2axx9/nLq6OqvCFrFMTa2bd5dlk9yjA5cM7G51OOIldrs4W3TkX8Vgx0jLYlAxKCK2U7JlK/kffNjwz88X1a3/p2TLVqtDlSC0Zs0aVq9ezeLFi/nwww/Ztm0bS5cu5bHHHuPVV1/ln//8J1lZWaxatQqAadOmMXv2bJYvX45pmixcuNDiIxDxvaWrcyg6Usnt49K0wHwQs9vFWX+4M6gGMiJiO/WL6ta5ynFERuJwOnE4nbhraynIWI6nogJndBRRvSZbHaoEobi4OB599FHCwo61Ek9KSiI3N5eePXuSmJgIwLhx41i2bBnJyclUVVWRnp4OwLXXXstLL73ETTfdZFn8Ir5WXlnLP1b8wODUeAYkx1kdjrShki1bce3a3fD45xdn60UnJ9FxQH8rQvSqoiMVOBwGse3DLYtBxaCI2E79orq7X3uTqgMHGi2qS0U5kSlJJN1zt9ZSEq9ISfmpHX5ubi4ZGRnccsstxMX99CM3Pj6ewsJCDh482Gh7XFwchYWFPo1XxGoffbUHV2Utt16pBeaDjd0vzh4qqaRLh3BCLLzbrWGiImJLWlRXrLZz507uuOMOpk+fTmJiIobx048B0zQxDAOPx3PS7SJ24aqsZcmXuxmalkBSj45WhyNtrP7ibHRyEg6H0ejirMNhEJ2SRNqc2cSmD7Q4Uu8oKqkkLta6+YKgYlBEbOz4RXXrykpxV1SAYeDak2N1aBLkMjMzmTRpEo888gjjx48nISGBouNathcVFREfH3/C9kOHDhEfH29FyCKW+N8vd1NeWcuNo86zOhTxEjtfnC06UkmchctKgIpBEbGx+kV1Y1KS6Tv7cWJ6J1PnclG84RurQ5MgVlBQwH333cf8+fMZO3YsAAMHDiQnJ4e9e/fidrtZunQpw4cPp3v37rRr147MzEwAlixZwvDhw60MX8Rn6u8KXthPdwWDnR0vztbWeSgqqSS+k7V3BjVnUERsq35R3bNG/QrD4aD3ww9S+MlnlGzZYnVoEsTeeustqqurmTt3bsO2G264gblz5zJlyhSqq6sZMWIEY8aMAWD+/PnMmjULl8tFWloat912m1Whi/jUR1/upryqjptGp1odinhZ/cXZjukDSJx4HXkLF1GyaQvFG76hQ1pfq8PzigOHy/F4THrER1sah4pBEbGt1BmNF8+tX1Q3Ycwor7+3u7KSH154md4PTiEkwtohIuJbs2bNYtasWSf920cffXTCttTUVBYtWuTtsET8iquihiVf7mZY/66c262D1eGIl9nx4mxe4VEAEuNjLI1Dw0RFRCxgt4V1RUSa48Mvd1NRVReUcwXdlZVkPzMPd2Wl1aH4jdQZU0kYMwrDcaw0qb84mzp96hn2DFz5B10AdLf4zqBXi8FXXnmFsWPHMnbsWObNmwccW2x33LhxjBo1iueff77hudnZ2Vx77bWMHj2axx9/nLq6Om+GJiJiKbstrCsi0lRHK2r436/2cNGA4LwrqIuBApB38ChdOkYQ0c7agZpee/c1a9awevVqFi9ejGEY3HnnnSxdupT58+fzzjvv0LVrVyZPnsyqVasYMWIE06ZN4+mnnyY9PZ3HHnuMhQsXalFdEQkadl9YV0SkqZasqr8rGJxzBY+/GBh7/iCrwxGL5B90WT5fELxYDMbFxfHoo48SFhYGQFJSErm5ufTs2ZPExEQAxo0bx7Jly0hOTqaqqor09HQArr32Wl566SUVgyISNOy+sK6ISFNUVNWydPWxu4LndG1vdThtQhcD5edM02TfwaP86oKeVofivWGiKSkpDcVdbm4uGRkZGIZBXFxcw3Pi4+MpLCzk4MGDjbbHxcVRWFjordBERHzO7gvriog0xfJ1eymvqmPCZSlWh3JGTZ37V38xcN/7iynIWA4YOJxOwKAgYzn73l9M4fJPMD0en8Qt1jtcWkVltdsv7gx6vYHMzp07ueOOO5g+fTqJiYkYhtHwN9M0MQwDj8dz0u0iIsHEzgvrioicSW2dhyVf7mZAchd6nx1rdThn1NS5f7oYKD9X30k06IvBzMxMJk2axCOPPML48eNJSEigqKio4e9FRUXEx8efsP3QoUPEx8d7MzQREUvYcWFdEZGm+HJjPodLq7j2smSrQ2mS5jQC08VAOV7ewfpi0NplJcCLcwYLCgq47777eP755xk2bBgAAwcOJCcnh71799KjRw+WLl3KhAkT6N69O+3atSMzM5PBgwezZMkShg8f7q3QREQsY8eFdUVEzsTjMfngi12c07U955/nnzcEWjv37/iLgbjd4AjBcDhw7cnR97/N5Owro0N0GLEx7awOxXvF4FtvvUV1dTVz585t2HbDDTcwd+5cpkyZQnV1NSNGjGDMmDEAzJ8/n1mzZuFyuUhLS+O2227zVmgiIpax48K6IiJnkvl9IT8eOMrDN53vt1OFWtsITBcDpd6u/BKSenT0i/+ve60YnDVrFrNmzTrp3z766KMTtqWmprJo0SJvhSMi4hdSZzReQLd+Yd2EMaMsikhExHrvr9xFl44RXJre3epQTql+7t/u196k6sCBRnP/qCgnMiWJpHvuPuWQT10MFIDqWjc/Fh7lgjT/GBrs9QYyImJPTe2yJiIi9vb93mK27TnMb0Yk4Qzx/U/T5uSr1sz9S50xlYQxozAcx46x/mJg6vSpp9wnUNnhN0BLjzF3fykej0lyjw5eiqx5VAyKiFc0tcuaiIjY2wcrdxEdEcqoodasudbcfKVGYGdmh98ALT3GXfmlACT16OiNsJrtjMXgydb727Vrl1eCEZHg0ZwuayKBSjlSpHX2F7lYl1XAVRefS0Q7r81eOq3m5qv6uX8xKcn0nf04Mb2TqXO5KN7wjZcjDRx2+A3Q0mPcnV9C+6gw4jpGeCmy5jnlf3UlJSUA3HXXXbzzzjuY/7oVXldXx/3338+yZct8E6GIBITWdlkTCSTKkSJt4+OvcwhxGFx98bk+e8/W5ivN/TuRHX4DtNUx7sovIdlPmsfAaYrBRx55hK+//hqAoUOH/rSD08no0aO9H5mI+AV3ZSU/vPAyvR+cQkjEqa9itbbLmkggUY4Uab3K6jo+++ZHLhnYndj24T5739bmKzUCO5EdfgO0xTHW1Lr58cBRhvQ5y4eRn94pi8G33noLgJkzZ/LMM8/4LCAR8S/Hj4mPPX/QKZ/X2i5rIoFEOVKk9b7IzKOiqo6xl/juriAoX3mDHc5pWxxjbkEZbo9Jsp/MF4QmzBl85plnKCwsZPv27Wzbtq3hHxGxh+aMiW9NlzWRQKQcKdIypmmy9Oscknt04LyzY33+/spXbc8O57S1x7gz79gUA39pHgNNWGfwhRde4M9//jNdunRp2GYYBitWrPBqYCJijdaOiT++yxpuNzhCMBwOXHtytKiuBB3lSJGWydp9mB8PHOWB3w6ybO6U8lXbs8M5bc0xbs85TKf24cTH+kfzGGhCMfjRRx/x+eefN0p0IhK8Wjsmvr7LWsf0ASROvI68hYso2bSF4g3fBE0iEKmnHCnSMku/3kNMZBiXDrJukXnlq7Znh3Pa0mM0TZPtew6T1quz3zSPgSYUg7GxsUpyIjbS2jHx6rImdqIcKdJ8RUcqWZd1gPEjkmgXGmJZHMpXbc8O57Slx3jwSCWHSqtIO7eTjyJtmlPOGayf99C3b1+efvppNm/erPkQIgHMXVlJ9jPzcFdWnvG5rRkTnzpjKgljRmE4jn291HdZS50+9ZT7BKrmnNNAZYdjbAnlSJGWW7YuF9M0ufIi3zaO+Tmr8lXNkSNsmHQnNUeONHtff/9Obu05bc3x+erctPQYt+05DEDfXp29Gl9znfLO4JQpUxo9/vzzzxv+XfMhRAJPU7uC1rPDuP/Wau45DUR2OMaWUI4UaZnaOjfL1+VyQd8EzuoUaXU4ljiw/FNqS0oo/HQFiROva9a+wf6d3Jrj8/dzsz3nMFERofRMaG91KI2cshg8PrGJSOA7vitoU74k7TDuv7Wae04DkR2OsSWUI0Va5uvN+yl11TDWh4vM+5tDX60G06Ro1ZfNLgaD/Tu5Ncfn7+dm257D9DmnEw6H/8wXhCbMGZw5c2ajx4ZhEBERQUpKCtdffz0hIdaN9RaRU2ttV1A7jPtvrtae00Bgh2NsS8qRIs3z6YYfSegcycCUOKtD8Zn8xUso3bq14XFlwQEIDaWy4ADbnnq6YXuH/v3pMf6aRvsG+3dya44vkM5Nqaua/IMuLh+SaGkcJ3PGYhBg+/btjB8/HofDwdKlS+ncuTOFhYXs3LmTJ554wtsxikgLtLYraOqMxmPf68fEJ4wZ5Yvw/VJrz2kgsMMxtjXlSJGmOXC4nC27DnHLlal+d3fEm8y6Oko2bQa3BxwOMAwcDgcej4eSjZvB44EQB+37pp24b5B/J7fm+ALp3GzPOTZfMM3P5gtCExad3717NwsWLGDSpEncdtttvP3225SUlPDyyy+zbt06X8QoIi1Q3xU0OjkJh8No1BXU4TCITkkibc5sYtMHWhxp4LDDObXDMbYl5UiRpvvsmx8xDBg55GyrQ/GpxOsn0G/OkzhjosGgUeMRDHDGRNNvzpMkXjf+hH2D/Tu5NccXSOdm255iQp0OUhL9Z7H5emcsBktLS4mOjm54HB4ejsvlwjAMQkNDvRqciLROa7qCysnZ4Zza4RjbinKkSNO4PSYrvslj0HnxdOnoPwtu+0qH/v1If/F5DBrfETUwGPTyC3To3++U+7bFd7I/dyFtzfEFSr7K2nOI3mfHEur0v6kDZywG09PTmTp1KuvWrWPt2rVMnz6dAQMGsGrVKiIi7Pcfs0igOb4raF1ZKe6KCjAMXHtyrA4tYNnhnNrhGNuCcqRI02zeWcShkkquuMBedwWPd3jtWgBMjwdPbS2mxwPAoa/XnnHf1n4nH99p0x+15vj8PV+VHK1md34pg3r75zzZMxaDc+bMoWvXrjzzzDM8++yzJCYmMnv2bMrLy3nqqad8EaOItEJ9V9CYlGT6zn6cmN7J1LlcFG/4xurQApYdzqkdjrEtKEeKNM1nG34kJjKUoWn+cafGCgc/X4XpdhMWG0vqo1MJi43FdLs5uPKLM+7b2u/k4ztt+qPWHJ+/56tNO4sAGHRevMWRnNwZG8iEh4fzyCOP8MgjjzTaftVVV3ktKBFpO+oK2vbscE7tcIxtQTlS5MyOVtSwdmsBV150jl8Ok/OV2pIjdBl+KSkP3I/D6SR28GB2vvgKZdu2nXHf5n4nB1KnTWhdzvH3fLVxx0FiIsNI6uF/8wUBDNOsH2Db2I033sj//M//MGjQIAzjp/HNpmliGAbfffedz4I8nfz8fEaOHMmKFSvo0aOH1eGIiIgX+ct3vnKkSNMtXb2HNxZv5aVHfsm53TpYHU6bcVdW8sMLL9P7wSmE+Nmw8CMbN7Hn9TcbddoMiYjAXVmJp66uodNmr3sne6XBSs2RI2x6aBrpzz9LWGxsk/fz53PaEqZp8rs5y+mf1IVptw4BvHeMLf2+P+WdwRdffBGApUuXtj46EbGdliYCCL5kcDItPUY7nJtAoBwp0nSfbviRpB4dgqoQhMbz8PxtkfP6Tpu7X3uTqgMHGnXapKKcyJQkku6522sNVg4s/5TakhIKP11B4sTrmryfP5/TlsgtKOPI0epGQ0T97RhPOWcwPv5Y0N27d2fr1q0sXLiQTp06sXHjRrp37+6zAEUkMB2fCJrL3ye6t4WWHqMdzk0gUI4UaZqc/aXs2VfKFb8IvsYx/j4Pz8pOm4e+Wg2mSdGqL5u1n7+f0+b67vuDAAw676fmMf52jGecM/jmm2/y9Qu2jlcAACAASURBVNdfc+DAASZNmsQrr7zC3r17ue+++3wRn4gEqOMTQXOuCkLjL0p/uGrmDS09Rjucm0CiHClyequ+yyfEYXBJeuBfJAm0eXjQuNMmbjc4QjAcDlx7cuiQ1rfN3id/8RJKt25teFxZcABCQ6ksOMC2p55u2N6hf396jL+m4XEgntPm+G7HQRJjw6hcsZz8f23zt2M8YzH48ccf849//IOJEycSGxvLwoUL+e1vf6tEJyKNtDQRQPAnA2j5Mdrh3AQy5UiRUzNNk6827SO9dxwdottZHU6rmW43hcs/aTQPz+F04q6tpSBjecM8vKhek70WQ3OnCtR32uzQL43a8nJCo6IozdpG8YZvmlQMNvX9zLo6SjZtBrcHHA4wDBwOBx6Ph5KNm8HjgRAH7fumNd7PD86pt1RV17E9p5grekdTuPx//fYYz1gMOp1OwsLCGh63b98ep/OMu4mIzbQ0EUBwJ4N6LT1GO5ybQKYcKXJqO348wsEjldw8JtXqUNqE1fPwoPnzzeo7bYZ16cyuV16l+5T76Jg+sMmdNpv6fonXT6B96nl8/5/PUldRgeE4NhPNcDgwPW6cMdGkzphGh/79Gu3nD+fUW7L2HKbO7WHYpWn0ubqP3x7jGdcZ7Nq1K1988QWGYVBTU8Nrr72m+RAicoLE6yfQb86TOGOiwaBRIsAAZ0w0/eY8SeJ140/Ytz4ZRCcn4XAYjb4oHQ6D6JQk0ubM9krHM19p6THa4dwEMuVIkVP7cuM+Qp0OhqZ1tTqUNmPlPDxo/nyz1BlTSRgziuIN32DWuSlev4GEMaNInT61zd+vQ/9+pL/4PAZGo+0GBoNefuGEQrCe1efUWzKzCwkLDSGtV2e/PsZTXr50uVxER0fzxBNPMH36dHbs2EF6ejoDBw7kueee82WMIhIg6hNB5l33NNpenwhO11W0/oty80NTj31BGkajL8qmDIfx906bLT3Gtjg30raUI0VOz+0xWb1pH0P6nEVURKjV4bQpX83DA99PMWjt1ITDa9cCYHo8mG43GMdKw0Nfr6Xb1adef9WX59QX/j97Zx4fVXX3//e9s2SSyb6yJKwhJIQlEBFRAREBpViePlV+Lm0f9alLrfu+VSqttWpdUSttn/Zpq4+KVrG1RaSAiKyyE2QngezJJJkks8/ce39/DBOSkD2TZJKc9+uVV8jNPXPOPTfMme+538/nq2ka2/NKmTY+CaPBX1szVK+x1WBw5syZ5Obmctlll/Hzn/+cIUOGoCgKkZGRvTk+gUDQz+jqQgDdf6MMNbvmlujqNYbqIjJYEWukQNA2h05ZqKl3M3vqwHtSHtDhxeZMJm3pNRSu+gjrvgMd1uF1ht6WGHRXmlCxYROaomBMTGTMbbdw6nd/xGOxULHxyzY/A/TmnPYGxwutWGpd/HDRuafioXqNraaJbtq0iaVLl3Ly5EluvfVWlixZwmuvvcaWLVvwer29OUaBQNCPaFgI4uLIfOwhjHFxaIpCxcYv220beKOMGpfOhKefJCojHZ/NRvXObzrUd6jZNbdEV6+xu3MjCC5ijRQI2uarvcWYjDouyErp66EEnYAOL+OB+4gYMYKMB+5j5A9uwF1ZGfS+elti0F1pgtdaQ+LsWeSufJOEGTPIXfkmibNn4a2pafM6e3NOe4PteaXIssT0CefSP0P1GiVNCySutk1xcTFbt27l3Xff5fTp0+zdu7enx9YhioqKmDdvHuvXryc1NbWvhyMQDHq+ueVWorOzGXfvXch6ParPx/HX3qDu0CGm//H3bbY98vxviJ0ymZQFV5wVnauUf/FvrAcOtKhvaJ7OUr7u30gGI5rXQ8r8KxqOh5LTZmevsbvtBhqh+p4v1kiB4Bw+ReVHP/+caeNTeOgHuX09nKBjsTrZuLuQzfuKsbt8mE16ZuUMZ25uGomxPZO273M42X//Q0hGA67iEkzDh6F5vOS8+ps2pQKBdnJ4eMPaoTqdPdZO4OfOF9YTF2Xi2Z9c0mt9dvX9vl3Ls8LCQjZs2MCWLVv49ttvyc7OZunSpd0arEAgGLg0D/hkvZ7xD97XobaZjzYNaiRZZsiVCxhy5YIWz++PTpudvcbuthP0LN1ZI202G9dddx1vv/02J0+e5OWXX274XXl5OVOmTGHlypW88cYb/O1vfyM6OhqApUuXcuONN/bI9QgEwWDfsUrqHd4BmSJ6OL+aFav24lVUTAYdUeF6FEXjix2n2birkLuXTiVrdHzQ+w1IBTzV1WiKgqe6BkNUVI9JDIQ0oesUVdRTWG5j0cWj+3ooHaLVYPCVV15hw4YN2O12Zs2axQ033MDMmTMJC+v/dWIEAkHHCWVTlr62pPbU1LDv/ofJeeXFNs1xWiKU5zVYDORr7O4auX//fp566ikKCgoAmDNnDnPmzAGgsrKS66+/nscffxyAvLw8Xn75ZaZODU0drEDQnGf+sB2AqeOT+ngkwcVidbJilf+pf2QjUxy9XiJSL+P2KKxYtZflt18c9CeEAamA5lMAfzmngFSgreCsqzq1UNW39Qe2HSwF6Dcuuq0GgytXruTyyy/ntttuIycnpzfHJBAIQohQN2XpS6fNsrXr8FqtlK9bT9rSazrVNtTnNRgM5Gvs7hq5atUqli1bxiOPPHLe71544QWuu+46Ro0aBfiDwZUrV1JcXMz06dN59NFHxcasIGRRFBWAhBgTBr2uj0fTMTRVxVNTg7uiEnelBW+tFZ/Njs9mO/vl/3d1ZS2LHG50aEhoSJr/C8CnM+DTGXBJBvYt38TIkUnozJGEJSUQlphEWFIiYUmJGGJiGkovdYSiTz6l9uBB7CfzMcTF4CwsBkB1uwlLTqRy01c4CguJmTSJ1O8tOa99QKcWkBhkPHBfg8SgLbraTuDXC6anxZIU1z82QVsNBj///HM2btzISy+9REFBAZdccgmXXXYZl156qXBLEwgGEY1NWUL1A31fpbNYNn8Nmkblpq86HQz2h3ntLgP5Gru7Rj777LMtHi8oKGDnzp0Nv7fb7WRlZfHwww8zcuRIHnvsMd566y3uv//+oF6PQBAsDhdUA3DrktDQaQfQNA1PVTWOM2dwnD6Do6jIH/xVVOK2WNB8vvPa6MwR6M2R6CPN6CMjKSccLcIMsoQmyf6QUJKQ0NApPgyKhzCfB09pKbV15XjrbaguV5PXlAwGwocOwTxmtP9r9GjMo0dhiIpqedw+H9Z9+0FR8dbVNalR5ywqAVXFum8/0ROyW2wvpAm9S1Wtk2NnrPzwqqy+HkqHaTUYHDVqFDfffDM333wzdXV1fPXVV6xbt47nnnuOMWPG8Kc//ak3xykQCHqJ7tYY6gt6K50lsEMbwFlaBgYDztIyDi3/ZcPxlnZo++O8dpbBcI0BemqN/OCDD7jhhhswGo0AmM1mfv/7czrcW265hSeeeEIEg4KQZcehMvQ6uU9TRDVFwV5wmvqjx7AXnPYHgGfOoNgdDecY4mIxJacQOW4sCRdfRFhyEmFJSZiSkzDExqE3RyDpmj7ZfOXZdUSF65EkqXmX5/rWNGxOH394cj6apqHYHbgt/ieO/q9KHIWF1B7Mo/LLrxrahaUkEzt5MrFTpxAzeVJDcBg1PoOUefOo+HITmtfTrDMVyWgk+bI5RGWkB2HmBN1le14ZADMn9Y8UUeiAgQxASUkJ1dXVeDweDAYDcicebwsEgv5FKJiydFZrFkhnSZx1Ccdff5Nx9/wUy+YtHU5n6Wh/jXdokWWQJGRZRlVVrHv3g6qCTm5xhzYU5rWnGQzX2BLBXCPXr1/P//zP/zR57a1bt3LNNf4nz5qmodd3aOkWCHodTdPYkVfG5HGJRJh6r9C8x2ql/shR6o8eo/7oMWwnTqK63QDoIyOJGJFG0uxZRIwYQcTINCJGjGj1SVxbmE1+sxi9vvVgUFE0Ikz+/6OSJJ19qmjGfDbtu+m4a7Hn52M/lU/90WNYtmylfN2/QZL8m2ZTcwhLTKT2wAFkgwHFczYYDMghNJANBmoPHCDhkpmdvp6eZCBrxtti64EShidFkpbS+b+vvqLVFeWvf/0rO3bs4JtvviEmJobZs2dzzTXXMGPGDExnTRoEAsHAo69NWaDzWrNAOkvN7j3UH/V/IOhMOktH+0u79vtEZ47nyPMv4nM4GnQfftttBX1UJJmPPkzMpInntQ2Fee1pBsM1BuiJNbK6uhqXy0VaWlrDMZPJxIsvvsiMGTNITU3l3XffZf78+cG6DIEgqBRV2CitsvMfl43t0X58Nju1B/Ow7t+Pdf8BXCV+ww5Jr8c8ejQp8+cRNX48UeMzCEtOavNJXmeYlTOcL3acJlLf+oaPy6uwYMbIDr2eMTYG49Qc4qb6dceaolB/7DjW/Qew7t1H0Ucfg6oSMSINqbH+slFVOENsLNlPPxFy76sDWTPeGlW1Tg6etHDd/PF9PZRO0WowuGnTJmbPns1DDz3UIGIXCASDg740ZYGua816o13MpInkvPYKu2+9o8lxCYmpK15t01W0r+e1NxgM1wg9s0YWFRUxpNkHuvj4eJYvX85PfvITvF4v06ZN4+abbw5KfwJBsNlxyJ8id+GE4AYmqs9H/dGjWPcdoHb/AeqPnwBVRTaZiJmYzZCFC4gan4F5zGh0PWiuNDc3jY27CnF7FMKM55vjuD0KBp3M3Ny0Flq3j6TTEZ2VSXRWJiOuW4rHasWy+WsqNn6Fr7au4TxDXCze2jr/HIQZQy4QhIGtGW+NzfuK0TSYM61/1XRtNRj8wx/+0JvjEAgEIUZvmrJ0VWvW2+0CVG3bBvgd6DRFAUlCAixbtjFs8aI2r3Uw1G4aDNcYrDVyw4YNDf+ePHkyq1atOu+chQsXsnDhwqD0JxD0JDvyShmbGhOUsgqKy0XNnr1Ub99J9a7dKHY7yDJR49JJveY/ic2ZTFRGBrKh99JRE2PDuXvpVFas2ovN6cVk0KHTSSiKhsvrDwTvXjo1aGUljLGxDLt6McOuXsz2G36I4nKDpuGtsSLp9aDT4a6sDEpf3WUwacZbY9OeItLTYhme1L+MNoXwQCAQtEhv1hjqqtast9sFqNiwCU1RMCYmMua2Wzj1uz/isVio2Phlu8HgYKjdNBiuUSAQNKWm3sXRMzVcvyCzy6/hraujeucuqrbvoHb/AVSPB31UJAkzphN/4XRiJk1CH2k+r53F6mTj7kI27yvG7vJhNumZlTOcublpQa/3lzU6nvuun8Y7aw5z9EQZC0s2s3bYLManD+EHV2WRMaJzNWc7SsoVV1CxYSPR2Vk4i0rwVFWhOJ1IYWFUbdtO/EUzgpYO2xUGq2Y8QFFFPSeKavnv754vEwl1RDAoEAhapDdrDHVVa9bb7QJ4rTUkzp7FuHvvQtbricvN5fhrb1B36FC71zoYajcNhmsUCARN2fVtOZoGM7I7l7Loczip3r6Dii83UXswD1SVsKREUhZcQfyMC4nJnnCeq2djDudXs2LVXryKismgIyrcb/LyxY7TbNxVyN1Lp5I1Or67l9dif1P0tYzwWJhsqKWgKopX39sT9P4CBN5XjQkJnHjzLcY9eB81O3dh2fw1R379ItETshh96y1EjhkT9L47wmDSjLfEpj3FSBLMnjq8r4fSaSRNa6RC7YcUFRUxb9481q9fT2pq/8rRFQhCmd7caQ3gczjZf/9DSEYDruISTMOHoXm85Lz6mza1ZoF2cnj4WTMXFdXp7LF2gr5DvOd3DjFfgt7il3/cwcniWv741Px2n1BpioJ1334qvtxE9fadqB4PYSnJJM2eRcLMizCPGd2hp1wWq5OnV24FaFXDB7D89ouDsm4172/q0Q0MrzxJcVI6e8fPDXp/LXHirbep2rqdhIsvIv3OO/xP5P69njPvvo+3vp5hV3+HETdch66PzB4H47qqaRq3P7ee5PhwfnnHJX02jq6+34sngwKB4Dx6e6c1QEBr5qmuRlMUPNU1GKKi2tWadVWjNhi0bQKBQNDT+BSVAycszJ46vM0gznYqn4oNG7F89TXe2lr0kZEkX34ZSZfNISpzfKfTHDfuLsSrqESGt6wbDDPqsDm9bNxdyLXzMjr12i2xZfVGMgrzCDP4A89kazEuo5lkaxEZZ3YD4PYqbFntZslNbUsGOkpHtXhjf3oHNbv3UPLpP6je+Q0Z999L1PjuX3NnGYzr6vFCK6VVdpZeMa6vh9IlejwYtNlsXHfddbz99tucPHmSl19+ueF35eXlTJkyhZUrV/LGG2/wt7/9jejoaACWLl3KjTfe2NPDEwgEzbBYnaxYtRegyQKr10tE6mXcHoUVq/b2yM5nQGum+fy7q5rPh89ma1dr1lWNmtC2CQQCQfc5fsaK0+1jakbyeb9T3G4sX2+h7PMvsB07jqTXEz/9ApIum01c7rRuGcBs3leMydB6CimAyaBj877ioASDeccrya08jNHnxqszoskyEZ563IZwxhQfxKB48OjD2HU8hSXd7s1PR7V4Y35yO+l33kHS7Fkcf/V1Djz2JCN/cAPD//M/elVLOBjX1S/3FGHQy8ycNKyvh9IlejQY3L9/P0899RQFBQUAzJkzhzlz5gBQWVnJ9ddfz+OPPw5AXl4eL7/8MlOnDg77WYEgVOntnVaAok8+pfbgQewn8zHExeAsLAZAdbsJS06kctNXOAoLiZk0idTvnb/EdlWjJrRtAoFA0H32Ha9EkmBSemLDMceZQsrWfkHFxk0odjvhqcMZ/eObSbpsTpcKvreE3eUjKrztj7I6nYTN6QtKf6cihuGYvIRpx78k0lkLmoZe8eHW+8vo1ESlsGfcHMo53+Smq3RWixczMZuc117mxJtvc/ov71B/9Bjj7rsHfUTvpGgOtnVVUVQ27yvmgqwUzK18bgp1ejQYXLVqFcuWLeORRx4573cvvPAC1113XUN9pry8PFauXElxcTHTp0/n0UcfJawHa8UIBIKW6e2dVvA/AbTu2w+Kireu7lxBXU3DWVQCqop1336iJ2S32D5QdD6AJMsdKjrf1XYCgUAgOMe+YxWMTY0lyqTDsmUrpf9cQ92hb5H0ehJmzmDIlQuJzp4Q9CdUZpNfwqDXt/66iqIRYQrOx12zSU+dGs327EVcset9DD4XaBoGnxuf3sS27KtwoydCbvs6FaeTY6+uIOO+uzuko+ts/Va92cz4hx+gNHM8+X/6M3lP/oysp54gLCH48o7mDLZ1dd/xSqz1bi7rZ7UFGyP35Is/++yzXHDBBecdLygoYOfOnfzoRz8CwG63k5WVxcMPP8wnn3xCXV0db731Vk8OTSAQtILd5UOna3sh0+kkHK7g7LQCRI3PIGXePCSjETS16S81FcloJGXePKIy0oPWp0AgEAi6j8Pl5VR+BXPcJ9h9x10cfeEl3BYLI//rh1zwP79j/EMPEDMxu0dSFWflDMflVdo8x+VVmJUTHIfHeQlOxp7exaSTX6P3uTEqHjRJwqh4MPjcTDq5hbGndzEvwdnm69R9e5j6o0epO3ykw3031uL56mpRHA6QJGyn8ls8X5Ikhn13MROeehxnSSkHH3sCZ2lZp65X0D5rt58m2mxk+oT+65Lao8Fga3zwwQfccMMNGI1GAMxmM7///e8ZO3Yser2eW265hU2bNvXF0ASCQU9gp7UtgrnTCn5NRO2BA37tSKDrwAcHDWSDgdoDB9BUtdXXEAgEAkHv4q6qYv9b/8Mdpz4iaeu/MCbEk/nYI+T+9g1S//M/MMbG9Gj/c3PTMOjkBhfP88bn8ReCn5ubFpT+poxNILPiMCPLDmP0ufBJemymWHySHoPPxaiyb8msOExOo3TZlqjasRPNp1C1fUeH+w5o8aLGpTPh6SeJykhv0NS3RVzuNCb96hcoThd5T/wMR1Fxh/sUtE1NvYudh8q4/II0DPo+CamCQp+MfP369SxadM5lqaSkhI8++qjhZ03T0OuF0alA0Bf09k4rnNNEGBp/cGhU9cYQG0v2M08TlzMlaH0GA8Xp5PBzL6A4294FFggEgoGE48wZjr+2gt233Yny1b85Yx5K1q9+yeRfP0vCzBlt1gUMJomx4dy91O81YXN68flUNE3D51OxOb0A3L10atDMzkbNmkHqw4/h1YXh1pmwmaJRdDpspmjcOhNeXRipDz/GyEsvbNLOeuAgRR+vbvhq7gga+LIeONhq3wEtXsYD9xExYgQZD9zHyB/cgLuyst1xR44dw8Rnl6MpPg4tW4670tLtuRDAxl2FKKrGghkj+3oo3aLXI67q6mpcLhdpaed2aUwmEy+++CIzZswgNTWVd999l/nz5/f20AQCAf6d1o27CnF7lFbrNgVzpzWAacgQZONZnbBOhykpEVelBRQFOcwYkoVqG6f6xE0T5lcCgSD0CGbNWHvBaQo/+JCqrduQTSaGLFzA2+XxmJJTiM/O6qEraJus0fEsv/3ihmu0OX1EmPQsmDGy3WsMzM223fnkfruO3RPmMzN3dJvtJk4fT/Ibr3P40Udx+kBVQZbAEBlB1gsvkDwk7rw2AUdQT70Nj2zE4VPxSD6Mmgfbx59hVD0YoyIxj7m91bEm3v5T/zW+sqnRfRzF3NtmdWiezCNHMOHnPyPvyWUc+vlyJj//HPrI9o1unKWl7L37fqaueIXwoUM71NdgQNP85bYmjI4nLaVzhkid1Yz2NL0eDBYVFTGk2Ye6+Ph4li9fzk9+8hO8Xi/Tpk3j5ptv7u2hCQQCzu20rli1F5vTi8mgQ6eTUBQNl9cfCAZzp7UxnioLssmEISYGTVEIS0zEW1uLxxKau5iNU31EMCgQCEKNYNWMtecXUPjBKqq27UAXHk7qtd9n2Hevpk7Tc3j5F9x8aVIvXE3rJMaGc+28jE6ZmjWem9H1JaTYy0mwlvDFDrnduQmrLiXSHIZZVc/W0tMjyTJhVaXQQjAYNzWHsB/fy6kVvyXSVYtPb0SWwKcZcHg8VJjiyfrxT4jLGd/uWLtzHyPHjCHrycc4tGw5R3/zMhN+9kS7T3HPvLcKzeul8IMPybjvnnb7GCwcOlVFcaW9S0Z6obaR3Ctpohs2bCA11e+yM3nyZFatWnXeOQsXLuSzzz5j7dq1PPfccw16QoFA0PsEdloXzBiJJPttuSVZYsGMkSy//eIeKTgPkDx3LrLBQFTGOU2EbDCQfPncHumvswQj1UcgEAh6g+Y1Y/V6GUmS0OvlhtJBK1btxWJtPc3dduoUh3/1PPvuexDr/oOkLr2G3N//lpE/uAFDdBQHTvg36qaM69tgsLM0n5vUmnwkVSWtuqBDc9NZ/Z7F6uStDUVszlyI3GCk4/8uSxKbM6/krQ1FLfYXjPvYmJiJ2Yy941ase/dx+t332j2/5ptd/mtuR5s42Fi74zQRJj2XTOl8bcGuaEZ7EiHMEwj6CcFM9ekMesXD3OP+9JmeJqCJSJx1Ccdff5Nx9/wUy+YtIVOfqKPFf9tK9REIBILeoDs1Y53FJZz+67tUbduOzhxB2nVLGXb1d9BHRjY579v8aswmPaOH9axRTLDZsnojGYV5hJ0to5RsLcZlNJNsLSLjzG4A3F6FLavdLLlp0XntO1tLL3Avhrur0SQwKF5kTUGTZDRJIsVdRUFYcov3oidq/6bMv4L6Y8cp/ng1sZMnEdtIj3/8rbepyzvU8LPicPi/2x3svvPuhuPRE7MZd+cdHepvoGFzeNi6v4R5F47AZGw/lLIeOIjtxMmGn5tvJAeITB9L7ORJPTLmthDBoEDQDwhWikhX+uts+kx3CNQnqtm9h/qjR6k/eiyk6hN1tvivQCAQ9BVdqRnrqamh8P1VlH3xb2Sj8WwQuLhVbdnh/CrGj4pHbqeuXqiRd7yS3MrDGH1uvDojqqxDlXXIisKY4oMYFA8efRi7jqewpIX2na2lF7gXQ4vzMXrdlMencWTkdDJPf0NKdSFDLfmUjRraYv3enqr9O/rHt1B/5CjHX3+DqSteRW/232PN68NVXNL05LN1DRsfj8rsG41oKPDlniI8PpWFHTSOCfWN5P7rgyoQDBKCnSLS2f46mz4TDEIthaIxgeK/IJ1zPG1U/FcEggKBIBToTM1Yn8PJmf97n9133EX5uvUMuXIBuSvfZMT1/6/VQNDm9HKmvJ6sUT1fyDzYnIoYxubJS6iJSgZJwif71zafbABJoiYqhc2Tl1AQ0fkUwJYI3Auzq468MTPZmbWQOnMCO7MWkjdmJhHu+lbr9/ZU7V9dWBjp99yFp8ZKwf/+peF4xr13kfHAva1qCSWdjowH7iXjnjs71d9AQdM01m4/zdjUGMamxnaoTWAjOTJ9LLIsNdlIlmWJyHFj+9QxXTwZFAhCnJ5IEWmL7qbPdIVQT6FoTuPiv37zAB2SLGM7lU9M9oS+Hp5AIBA01IzV61sPJFSvj8nVh9lzx3t4a2tJuGQmI39wA+HD2g+Cjp6uRtPol8Gg2aSnTo1me/Yirtj1Pv4Ct1LD923ZV+FGT0SQnngG7sX2ic3WTEkif9hE8odNRPGpLdbvbX4fdT4P04+s45vM+Sh6v79GV2v/Ro1LZ9h3F1Oy+u+kzL+CqIxxACTNmY15bDp7f3r3eW2mvvnaoHYVPXqmhoLSOu78/uROtQtsJO+//yH/BrIkNdlI7ktXUfFkUCAIcTqTIhIM8o5Xkll5mPFndjOm+CCgEeGpBzTGFB9k/JndZFYe5uDx9msbdZRACkXx3z6hdM1aQELW6wGJ0jVrKf7bJ5Sv/SJkis53tfivQCAQ9Bbt1YxNrj7Dwj0fkHtiE+FpqUx+8ddkPvJQhwJBgMMF1ciyRMaI890zQ53A3MTaKhs0fGFeB0bFgyZBnK0yqPV0u1O/t3nbpNoSEurKSKwtbbdtR0j7f0sxxMWS/4c/ojWq71u6Zs25kxof/2wNg5l/bD5FhEnPZV0or9V4I9lXV+vXY0oStlP5PTDSjiOCQYEgxOmpFJHW1a/bVwAAIABJREFUaJ4+g6ahV3wNO1nBTp+B0E+haE53iv8KBAJBbzA3Nw2DTsbtaRqERLjquCjvX8w+8Ck6TWH4vfcz8ZfPNDwV6iiH86sZPSya8LD+l2QWmJvk8pMYvW5qopPZMvm7VEenYPS6Sa44FdR6uq3diwBt1e9t3nao5RSSqjLMcqrdth1BHxHOyBuvp/7oMap37mo4btm8BQDZaGT0rbcgn3X5r/z66y71MxCoqnWyZX8J8y8c2aW/+1DdSBbBoEAQ4gRSRNqiqykirfVXZ/Snz4CEwecGTfN/P5s+U2eMbrc/xenk8HMvoDg7pi3sT1q8zEcfYsiVC5Bk/1towDwg85GH2mkpEAgEvUOgZiz49X2a20NW/g4W7HyHIdWn2TfiQkb88teMuvxSJKlz6ZCKonLsTA1ZI/tfiiicmxuzp57dqReyddx8aiPi2TpuPrtTL8Tsrg9qPd3m98LnU9E0DZ9Pxeb0ArTan/7MCX6cUE5W0R5GntxJUk0RLmMESTWFjDy5k6yiPfw4oRz9mRNdHl/y5XMxDRlC4fsfNDwdVJwOzOnpXPjunxm2+Dtc+O6fMaenN7iLDkbWbC1A1TQWXzq6S+1DdSO5/23nCASDjFk5w/lix2ki9a3v3bi8Cgs66GrVHvMSnOTvyiPeZ0Pvc59Nm5EwKh4kJCad3EKN3szoCya2+TpdKaoqtHgCgUAQPLJGx/PMbTPZ/uFajP/+lEh3PWeS0jEu/j7/77JJXQ528kvrcHmUHqs52xtkjY5H9/MnOLLmMMWnqlBUDZ0sETdhOhdc9aOgp78G6vcGSkTZnD4iTHoWzBjZZokoTVHQvvmaC+pteGQjDgl86DBKClOtxzCqHrRv8tEuyG6zf2dpKXvvvp+pK145T/Mn6XSkXvufnFjxFrX7DxCbM4WLP3wf8DvM7r7tTnJeeZGcl54PzmS0geJ0cuzVFWTcd3ef6uia4/EqfL69gAsnDGFIQsumSu3RWRfa3kIEgwJBiDM3N42NuwpxexTCjOdrB7ubItKcKWMTCPvXYcI9NmRNxScbcIZFEe6ux+BzMarsW1KMkWSlX9bm6zR2BO1oMBhIoYjNmUza0msoXPUR1n0HqN75jQgGBQKBoJO4Ky1Urvw98d/sImJEGqNvfZBLgmDCdaSgGoDMfmgeE6BxCaXhiWZ0OglF0SitsvPqe3t6pIRSYmw4187L6JTZW/OSRtERZiRJQtM0VIcd09ARHSppdOa9VWheL4UffEjGffec9/uk2bM4/Zd3Kfnsn03qDpatXYfXaqV83XrSll7T8YvtIl3ZSO4NvtpbTK3Nw9WzxvT1UIKOCAYFghAnkF6yYtVebE4vJoOuYdFyef2BYDDTWUbNmoHNFEvZr38BmoYzzAyyjM0UTbjbDpJE6sOPMXL6+CbtguEI2tlCvgKBQCA4H01VKVuzloK/vAOqyqibfsTQq79z1pir+xwpqCEhxkRSkNad3qZ5CaUAer1EpN6vz1uxai/Lb784aGtrdwiGE2XNN349YGv6NNloJGXhfIo+/BtuSxVhiQkAWDZ/DZpG5aaveiUY7MpGck+jaRr/+PoUI4ZEMTk9sa+HE3REMCgYEFiszobUC7vLh9mkZ1bO8DZTL/oTXU0vgXNzs213PrnfrmP3hPnMzB3dZruJ08eT/MbrHH70UZw+UFWQJTBERpD1wgskDzk/fSbgCOoJpLL4VDySD6PmwfbxZxhVD8aoyDaLqibe/lP/Nb6yqdF9HMXc22Z1ftI6QVvpM4KuE6rpPgLBQMZxppATb/6W+iNHic2ZwtiftP/UqLOcLLaSnhrbaa1hqNDbJZuCQWdlFMffepu6vEMNPwe0fordwe47z5WMiJ6Yzbg77wD82sGiVR+R9/Syhr8ZZ2kZGAw4S8s4tPyXDe1iJk0i9XtLun1d/aG01Lf51ZwqruWn10zpt3/zbSGCQUG/p3Gqh8mgIyrcb7jyxY7TbNxV2COpHn1BV9JLGs/N6PoSUuzlJFhL+GKH3O7chFWXEmkOw6yqZxcePZIsE1ZVCi0Eg3FTcwj78b2cWvFbIl21+PRGZAl8mgGHx0OFKZ6sH/+EuJzxLfTWt/exvfQZQdcI1XQfgWAgonq9FH30MUUffYwu3MS4e+8mae6coH94dbl9FFfaglZ2oS/oTMmmUAkGOyuj0Lw+XMUlTQ+efaLY+HhUZlbDv8OHDiEsKQlXcSmu4lKQZZAkZFlGVVWse/f7d4d1MtET2tYodpTARrLPZkeOiEDW65H1ehSvl9I1a1EdDvSR5jY3knuaf2w+RWS4gctyU/tsDD2JcBMV9Guap3ro9TKSJKHXyw07fitW7cVi7Zij5UCi+dyk1uQjqSpp1QUdmpvOWiBbrE7e2lDE5syFyA0fPvzfZUlic+aVvLWhqMX++vo+tpc+I+gajdN9BAJBz2E7cZJ99z9E4furSLh4JlPfeJ3kyy/rkacYBWV1aBqMHhYT9NfuLXq7ZFMw6KwTZca9d5HxwL1IupaDXkmnI+OBe8m4584mx4cuXgSAzhwBEk1cs5FAHxXJxGeWkXbN94JyXaFeWqqixsG2vFIWXjQSk3FgPkMbmFclGDT0x1SP3mLL6o1kFOYRdnb3M9lajMtoJtlaRMaZ3QC4vQpbVrtZctOi89p3Vr8XuBfD3dUNRXxlTUGTZDRJIsVdRUFYcov3orfvY1fSZwTt0x/SfQSCgYSmKBR99DGFH3yIISaGrJ89QfwFuT3aZ35xLQBjh/ffYDBQskmvbz0gDGbJpmDQFSfKpDmzMY9NZ+9P7z7vd1PffK1FWUT8jOkU/OnPDFvyXYo++LBpn0hMXfEqxrjgOq0GQxPZU/z9K389x0WXdK2cRH8gdP7KBYIu0B9TPXqLvOOV5FYexuhz49UZUWUdqqxDVhTGFB/EoHjw6MPYdTyFlrL+O7vwBO7F0OJ8jF435fFpHBk5nczT35BSXchQSz5lo4a2eC96+z52JX1G0D79Id1HIBgoOItLOPbq69iOHSdx9qWMvf1W9JGRPd7vqZI6zOEGkuL6rw64t0s29SWla9ac+0E7V7O49LM1jLn1lvPODx86lLDkZKq2bvM3UVU0RQFJQgIsW7YxbPH5G8jdJRRLS9XZPazdXsDsqcNJjovokzH0BiJNVNCv6Y+pHr3FqYhhbJ68hJqoZJAkfLL/qZtPNoAkUROVwubJSyiIGBaU/gL3wuyqI2/MTHZmLaTOnMDOrIXkjZlJhLu+1XvR2/exq+kzgrYJ9XQfgWAgoGkapf/6nH33PYirpJSMhx5g/IP390ogCHCq2MqYYTH92khjbm4aBp3fNbQlgl2yqTmK08nh515Acfa8hMWyeQvgdwsdfestyEYjAJVff91qm5hJE3GcKURTFIxxcWQ+9hDGuDg0RaFi45c9Ms7OSlN6g39+fQqXR+GaueP6bAy9gQgGBf2aQKpHW4RaqkdvYTbpqTNGsz17EX7tXmCe/KkX27Kvos4YHbS5CdyL7RMXkT9sov9JG4AkkT9sIjuyr2r1XvTFfUyaM5uc119t8XdT33yNpDmzg9bXYCKQ7gPSuV3oRuk+wXY1FAgGE+6qar79+S84tfL3RGdPIOf1V0iadUmv9a8oKgUldYzpxymicK5kE4DN6cXnU9E0DZ9Pxeb0AgS1ZFNzGptr9TSK04E5PZ0L3/0zwxZ/hwvf/TPm9PQGeURLRGVmgKoSN/0Ccle+ScKMGeSufJPE2bPw1tT0yDg7q4nsaZxuH//4+hQXThjCyKHRfTKG3kIEg4J+zayc4bi8Le/sBXB5lX7tetZVAnMTa6ts0PCFeR0YFQ+aBHG2yqDOTXfuRfO2Op+Hi/L+ic7nabdtd2grfUbQdRqn+/jqav0fOiQJ26n8vh6aQNBvqdmzl333PUjd4SOMueNWJix7irCE3nXKLq604fGpjBne/z8cB0o2LZgxEkmWsDl9SLLEghkjWX77xR1yr/bU1LDzph/j6WCAZLE6+XD9Mf7+u9XU17n4+8pP+HD9sU6Zo3X2qeLFH75PzkvPozv7RFBnNJLz0vNc/OH7rbaJTB8L+DdNNa+Xw8+9gOb1Mv7B+5j+x993eKydIfPRhxhy5YImhjVDrlxA5iMPtdOyZ/hix2nqHV6unTewnwqCCAYF/Zy+TvUIZQJzk1x+EqPXTU10Mlsmf5fq6BSMXjfJFaeCOjfduRfN2ybVlpBQV0ZibWm7bbtDV9JnBO0Tiuk+AkF/RfX5KPjzX/n2mV9ijItlyksvMPSqK/skTfNUSR3Qv51EGxMo2fT6g3P5w5Pzef3BuVw7L6PDTwTL1q7Da7VSvm59m+dZDxxkzx/+j/d+9gbFn6wm2VqEKyyCZGsRxZ+s5r2fvcGeP/wf1gMH2+2zN54qRqSlgSzjOHOmV59ihgpen8rqL08wcWwCmaP6f2my9hDBoKBf09epHqFMYG7Mnnp2p17I1nHzqY2IZ+u4+exOvRCzuz6oc9Ode9G87ZCKk0iqytCKkz16H7uSPiNon1BL9xEI+ivuykrynnya4o9Xk7JwPpNf/DURaX1X66ygpBa9TiItJarPxhBKWDZ/DZpG5aav2jzPWuug4ot1ZJfsJaviW5AkVFkPkkRWxbdkl+yl4ot11Na1/7SvN0r2yAYDpuRkXCWlg7JE0Je7C7HUurjm8oH/VBCEm6hgABBI9di4u5DN+4qxOX1EmPQsmDGSublpgzIQDJA1Op6kl5ezcXchhY3mZtR3r+qRuenqvbAeOEjUiZPcm+rlZJEVqa4Yhz6C5LoivqcrYGxqLOa9X2GtD25JguZpMoH0GUH36IoFukAgaErVjm848fobaIpCxoP3kzT70r4eEiUWO0MSzOh1g/NZQtEnn1J78NzTO2dpGRgMOEvLOLT8lw3HYyZNIvV753y6v/HEsTVzMZee2Uyks7aJoZtRU6mJSuHrEZdS746luX9pb5fsaehPlqk7fBg5LGxQlQhSVI2/bTzBmOExTBuf3NfD6RVEMCgYEARSPQZb+YjO0rZFS3Doyr1oXJIgKSICOc6MLjwcxelEPbEH6wEHtg6UJHCWlrL37vuZuuKVFusntYanpoZ99z9MzisvBr1+UmsoTifHXl1Bxn1392kNJYFAEFpoikLBn/9Kyaf/wDx2DOMffqBT72c9SXGljeFJveNaGopoPh/WfftBUUGWQZKQZRlVVbHu3Q+qCjqZ6AnZTdpt3leMFhXH1+OvZP6e9/FqPjRkJFR0GnyduRCfzthi+aTeLtkT6M9tqUJTVUxDhgyqEkHb80oprrTxyA8v6NeOuZ1hcG7tCASDhMP51Ty9citf7DiNpmpEhevRVI0vdpzm6ZVbOZxf3ddDBIJXkuDMe6vQvF4KmxXKbY+O6j6CyWDUYQgEgrbx1taS9/QzlHz6D4YsupLJz/8qZAJBVdUotdgZNoiDwbRrv8/EZ5ahj4oEiSZmJ0igj4pk4jPLSLvme03a2V0+vD4FX0khiiZhVH1EKC7CVC+KpuErLsLrU1osn9TbJXsC/RkT4kFVkcLCerS/UELTND5af4yhiWYunhycslv9AREMCgQDFIvVyYpVewGIDDeg18tIkoReLxMZ7k9RWbFqb6dczHqSYJQkqPlmF0CnjUo6qvsIJoNRhyEQCFrHdvIU+x98hPqjxxh3792Mvf1WZIOhr4fVgMXqxOtTGZ5k7uuh9CkxkyaS89or+Euwn0NCYuqKV4mZNPG8NmEGmYoaF2PrCwlX3ZSHJ7J6xHzKwpMIVz2MrT9DRY0Lo6Hlj+VN1kdVwVVWBqpCT5XsMQ0ZQvK8y/0/qKr/+yAoEfTNt+WcKKrlmsvHoZMHx1NBEGmiAsGAZePuQryK2hD4NSfMqMPm9LJxd2HIpNc2LkmAooCsQ5JlbKfyicmecN75x996m7q8Qw0/B4xfFLuD3Xfe3XA8emI24+68o+Hnruo+ukNv6z4EAkH/oeLLTZx882300dFM/vWzDdb+oURxpQ1gUD8ZDFC1bRsAmqqiKQpI/tDQsmUbwxYvOu/8xJhwiivtxPhsbEmaxsHY8SBJrBk2h0nWo6TZS1E0jcSY1iUDgfXRW1uH4nDgra1DZzK1uj52F1+d3zlWcTpQNa3d9bi/o6oa735+hKEJZi6/YHA50ItgUCAYoGzeV4zJoGvzHJNB16JGoa8IlCSIzZlM2tJrKFz1EdZ9B6je+U2Li4/m9eEqLml6UPI/WWx8PCozq2m7Luo+ukNv6z4EAkHooykK+X/6C6X/+IzoidmMf/hBjLGhWbahJBAMJg7uJ4MAFRs2oSkKxsRExtx2C6d+90c8FgsVG79sMRi01LqQJfjH8LnIjXVoksTBuEz2x45H1jQsta5W+wysjzpzBLrwcOQwY0PJnp4IzhyFRQCYR41k1A9vbHc97u9sO1jKqZJa7r9+2qAzSBLBoEAwQLG7fESFt/1fXKfzF9oNFQIlCVIWXIEky2Q8cB/lX/wb64EDLZ6fce9dxOVM5vhrfse95kg6HePuvYukObObHE+79vtEZ47nyPMv4nM4mug+NFVBHxVJ5qMPt5ju01UCOoyTv/0drrKyJroPHHYixo1l7B23Ddj0G4FA0BSfzcaR539D7YGDDP3OIkbd8l/I+tD9WFZssWMy6oiPNvX1UPocr7WGxNmzGHfvXch6PXG5uRx/7Q3qDh1q8Xy3VyE5LoJKqxNF1ZClhn1LVM3/7+S4CDze89exQFZJzb59RGdPwH76NMakJFSPm+jsCVj37qcoZnXQs0oUuz/TZvTNNxE+bGi763F/RlE13l17hNTkSOZM67vSLX1F6L7rCASCbmE26VEUDb2+9bx3RdGIMIXO20BXShIkzZmNeWw6e39693m/m/rma62aLwR0H7tvvaPJ8YDuoydcRQO6j/33P+T/FBD4NHBWhyFcRQWCwYGrrIxvlz+Lq7yC9Ht+SkpAn9UDWKzOhnI/dpcPs0nPrJzhnS4vVFJpY1hSZIcdFrvjmBzqbsujX36djbsLefO1zY3mdBFz/7vlzA6zyW/elhIfQVWtC8XpZFH5Fv6ZcgmG8HASYkzoZQmpBZ1a46wSxelC1sl4LBYM0VHYTuWjOhyUr/0i6Fklw/7juxz7zctoPv+G8UAuEbR5XzGF5fU88sMLBpVWMMDgeg4qEAwiZuUMx9XCLmNjXF6FWTnDe2lEPUfpmjXnftDOFdAo/WxNC2efo7HuQ/V60c4K5S1btgV/kGdprIv01dX6dY6ShO1Ufo/1KQhNbDYbixcvpqjIn471+OOPs2DBApYsWcKSJUtYt24dAFu3buXqq69mwYIFvPLKK305ZEEQqDtylAOPPI63to7s5U/3aCAYTEfpkkp7p8pKdMcxOZTdlrsyp7NyhlPn8FJe7cDrUxnlqWS4q5JRnkq8PpXyagd1Dm+L63FzN1FN01BdLjSNHnX3VD1uAOQwY1BfN9RQFJX31h5h1NBoLhlEDqKNEcGgQDBAmZubhkEn4/a0HBC6PQoGnczc3J4RSitOJ4efewHF2fNupZbNWwCQjUZG33oLstG/eFV+/XWb7Rp0H3FxZD72EMa4ODRFoWLjlz021oDuI2pcOhOefpKojPQG3Ydg8LB//36uv/56CgoKGo7l5eXxzjvv8Omnn/Lpp58yf/58XC4XTzzxBG+99Rb/+te/yMvLY9OmTX03cEG3sGzZSt5Ty9CFhzPp+V8Rkx08XfJ5fQXRUVpVNSpqHKTER3S4/+44Joeq23JX53TKuCTq7G40TUMnS6TbziCjkm4rRHc2wKuzu5kyLqnFfhu7ifrsdjRNw2e30ZPungFDNl1Ex+95f2Tj7kJKLHZuWJiJPAifCoJIExUIBiyJseHcvXQqK1btxeb0YjLo0OkkFEXD5fUHgncvndqpNKHO0HhnN27a1B7pI4DidGBOT2fSc79AZzSSsmA+Bx//GY4zp9ts11ndRzDorC5SMDBZtWoVy5Yt45FHHgHA6XRSUlLCE088QXl5OfPnz+euu+7iwIEDjBw5krQ0/6bN1Vdfzeeff86cOXP6cviCTqJpGsUfr+b0X94hKnM8WU88iiGmZ41igukoXWt3o6gaCTGt6wW745jcX9yWuzqnhzds49L6w7i9CmiQai/Dpg8n1V7KtMoDIEGYQcfhDXoybmpqQBOYG1dZOT6HHcXhBElCcTiRkMj/379gSkkJ+tz4zmoG9QM4GPT6VN5bd4z01Bgumjh49foiGBQIBjBZo+NZfvvFDXoRm9NHhEnPghkjO60X6SyNd3Z7Ohi8+MP3m/ysMxrJeen5dttN/+Pvm/ws6/WMf/C+oI6tOV3RRQoGHs8++2yTny0WCxdddBHLli0jKiqK22+/nY8++oiIiAiSks49LUhOTqa8vLy3hyvoBpqicHLl7ylfu47ESy/xbz4Zez71LpiO0lVWv8tlW8FgdxyT+4vbclfnNO94JbnVRzD63LhlA15JRkWHJqlMrTtKmOrFow9j1/EhNC9mFJgbt6UKTVGQjMazZmcqPpuNivUbCUuID/rcKA4HclgYkq7t6+3P/PubM1RUO/jJf17UYS3sQEQEgwLBACcxNpxr52V0uXyEp6aGffc/TM4rL7ZpqhLYvbQ7vZwssiLt3YlPM1D91U6O1esYmxqLOdzQod3L3jYPCHWzAsHAJy0tjTfffLPh5x/+8IesXr2ahQsXNvmQomnaoP7Q0t9QvV6OvfQqVdu2M/z732PkD25ocC/uaYLpKF1V6097TGijDl53HJP7i9tyV+f0VMQwHJOXMO34l0Q6a5F0YRglCTQZWXFTY05hz7g5lHN+2Y7A3Bx45Ak0TUUOj8BrsWBITER1+t2ws595Ouhz47M7BnSKqMvj44N1R8kcGUduZnJfD6dPEZpBgUDQJmVr1+G1Wilft77N8zRFofCzzyn++BPkXX4NH3r/jqK8awvFH39C4WefN5i0tEVvmweEslmBYHBw9OhR1q5d2/Czpmno9XqGDBlCZWVlw/HKykqSkwf3B5f+guJ08u0vfkXVtu2MuuUmRv3oB90KBC1WJx+uP8Y9L23kv59dxz0vbeTD9cda1fwFHKXbHGMHHaWr6tp/MghNtW2aouAqLT1b9qd9bVvjtg1GYI3clvs6EISuz6nZpKfOGM327EWABARew39927Kvos4Y3eq9MA0ZwrS330QfYUZxONA0DcXhQB9hJvftN3tkbjwWC2EJ8UF/3VDh000nqap1cdPi7EG/wSaCQYFA0CaWzV+DplG56as2z1NGj+eTkQuoMicjyRI+2Qj4v0uyRJU5mU9GLkAZ1f4Tyt42DwhVswLB4EHTNH71q19RW1uL1+vlgw8+YP78+UyZMoX8/HxOnz6Noih89tlnzJ49u/0XFPQp3rp68p5+htqDeYy79y6GL7m6W6/XVQfLYDlKV9W6kGWJ2Kj2awwGHJO9dbUoLie+2toOOyaHuttyV+c00C7WVokmgUHxEuZ1YFQ8aBLE2SpbvRfWAwcp+ng1Bf/7Z7x2e8OcKA4HXrud/D/9haKPV2M9cDCo1+oqK8M0tO8D8J6gps7FRxuOM3PSULLHJPT1cPockSYqEAiaUPTJp9QePLeoOEvLwGDAWVrGoeW/bDgeM2kSqd87p27YuLuQGkMUOyct4opd7xPY8Qx83zFpEbVeqUWzgt42D+gvZgWCwUNmZia33XYb119/PT6fjwULFrB48WIAfv3rX3P33XfjdruZM2cOV155ZR+PVtAW7qoqDi1bjqusnMzHHiZhxoXder3mDpYB9HqJSL3fMXrFqr0sv/3iJjrwublpbNxViNujEGY8X/fVGUfpqloncVFhHarBFnBM1pkj0MIjkE1hDY7JMdkTOtQ2NmcyaUuvoXDVR1j3HehQ296gq3MaaJdcfhKj1015fBpHRk4n8/Q3pFQXklxxiuJRQ1q8Fw2awapqNJ8PnSkMY2IiHosFxWajYkPwNYOqz4eropLEWZcG7TVDif/74ihen8p/fafv/6ZCAREMCgSCJmg+H9Z9+0FRQZZBkpBlGVVVse7dD6oKOpnoCU0t0QPC+qjaEnyahuRxI2sqmiThk2Wiaytwxwxr0aygt80D+otZgWDgs2HDhoZ/33jjjdx4443nnTNz5kz+/ve/9+awBF3EWVrGoaefwVtXx4SnnwzKZlJXHSyD6ShdVetqN0U0sMlm3buP6OwJOM6cwZiYiOb1EJ09AevefRTFxLS5yRbqbstdndNAu72/+JzdqRdyZvhEdHqZrePmM6I4j+H1xa3ei6aaQQ1jYgKSJBM2dCgeSxWSLAVdM+iurARVHZBPBs+U1fHF9gIWXTK6U3UzBzIiGBQIBE1Iu/b7RGeO58jzL+JzOBo0Ln73MgV9VCSZjz5MzKSJTdrZXT4MMkQXHSPM6+a0eSg7Eqcww7KfkfZSoouOUWJOwdeCZLC3zQP6i1mBQCDoPzhLSsh7ahmqx8vEXz5D1Lj0oLxuYKPNp2jU2z3UOz2oZ/fqosKNRJmNrbqCZo2O56bF2fzh0zwKyuobjg9NMHPT4myyRreuCbNYnQ1O1GfK6wkz6Phw/bFWnagbb7L5XK4mm2y2U/moDgfla79oc5Mt8fafsnF3Idt+s47cb9exe8J8ZuaOZu5ts9qco2NnanhnzWGOnihjYclm1g6bxfj0IfzgqiwyRrRufNYVskbHc9/103hnzWHyTlWhqP7agRPHJLTZX9boeJJeXs7G3YUUNnL3HvXdq9p19w5oBvff/1CTpBt9RAQ5r/6mQ8ZnnTFKc5WWARA+dGi7r9vf+NNn3xIepue6+eP7eighg9AMCgSC84iZNJGc115BomlKkITE1BWvnhcIAoQZZCpqXET7bGxJnsaa4ZdRbYpnzfDL2JI8jWifjYoaF0ZDy287TcwDVAVXWRmoHTMe6Ar9waxAIBD0DxoCQa8vqIEg+DfaPD6V4kobtXZp/CqaAAAgAElEQVQPaKCTAQ1q7R6KK214fCoO1/muoGu2FvDcn3dSXuPAoJMw6iUMOonyGgfP/Xkna7YWtNhnc42idjbgaUujGNhki0wfiyxLTTbZZFkictxYsp95mricKe32mVhTQoq9nARrSZt9Bq7x0Tc2s/+EheH2MoY7KxnuKGf/CQuPvrG51WvsKofzq3n1vT2UVtkZnmhm9NAohieaKa2y8+p7e1odJ5xz9379wbn84cn5vP7gXK6dl9Ghp7Pd1VN2xijNXuCv0Rue2r6etD+x71gFuw6Xs/SKDGIiw/p6OCGDCAYFAkGLVG3bBoCmqqheb4MLqGXLthbPT4wJR9E0/pV6OQfjMiHgziVJHIzL5F+pl6NoGoltWJM3GA/U1vnF8bV1PWoeEOpmBQKBIPRpEgj+4ueYR40M6uuHGXRU1PgLgOtk6azzof97QMNXUePA2Kz+3bEzNfxutT+90qCTkWUZSfJ/N+j8H/9+t/oAx87UNGnXXKOo00moGuh1ckOq6opVe1t0Me3qJlvzPlNr8pFUlbTqgjb7bH6N4+yFyKiMsxW2eY1dpfk49XoZSZLQ69ufm+4S0FNGjUtnwtNPEpWR3qDF7AidMUqzHTuGacgQDNHR3R12yKCoGn/8xyGS48JZfOmYvh5OSCGCQYFA0CIVGzahKQrGuDgyH3sIY1wcmqJQsfHLFs+31LqQJVC1lm23VU1DlvzntUZgsZPDjOjCw5HDjJ1a7DpLdxdXgUAwuOnpQBAgMcaEqoHciv29LPmDtcRmmr531hxG1UDXSjkLnSyjav7zGhPQKAYMUgJv6fLZwDPMqMOrqGzcXdji63Zlk23L6o1kFO5mUtk+Ms7sJtlajMtoJtlaRMaZs8cLd7Nl9cYm7f7517XkVuVxYfUhci0HSbOXYdeHk2YvJddy0H+8Ko9//nVtKz13juZz05z25qY7BPSUGQ/cR8SIEWQ8cB8jf3CDX9/XAgEX0sBXc6O0wFdLLqT1x44TmTEu6NfQl3y5u5D8kjr+6zsTzts4GewIzaBAIGgRr7WGxNmzGHfvXch6PXG5uRx/7Q3qDh1q8Xy3VyE5LoJKqxNF9Qd+0tnNYVXz/zs5LgJPC7bcAeOBmn1+4wH76dMYk5JQPe6zxgP7KYpZHXR3z1A3KxAIBKFLbwSCAJZaJzpJOruhdn5AqGoaOknCUtv0aVTeqSp07Zh/6iT/eY0JaBQDKKo/GpQbOYm2plGErjmC5h2vJLfyMEafG6/OiCrrUGUdsqIwpvggBsWDRx/GruMpLGnU7nSJlStqj2FS3Hh0RhRJRpV0aJLKFOsRjIoHly6Mf5cEp3xAg37Tp1Ln8GBzelFVzZ8GG24gOqJ1/WZjOqPfC5D56ENNfpZkmSFXLmDIlQtaPL+rRmluSxWeqmqixrdfBqq/4HL7+Ouaw2SMiO1QKZXBhggGBQOCxkJ3u8uH2aRnVs7wdkXZ3aUrb+jdadebjH75dTbuLuTN1zY3mtNFzP3vlsX/ZpO/7lVKfARVtS4Up5NF5Vv4Z8olGMLDSYgxoZclpBasyRsvWorThayT8VgsGKKjOmw80BU6u7gKBAIBgKuiolcCQQC3VyU5zkSF1YWiqk0CQlXTkCSJ5DgTHm9Tdy5F1dC3k/8lSeBTm2Zz2F0+osLPfTwMZHvoGvWr00nYnOdrFKFrm2ynIobhmLyEace/JNJZi0/2p1z6ZANGTaUmKoU94+ZQjrlJu/yIYXycNp95ZduJ8dbjlfzj9kp6JM1LeXgi61Muokof1fZEdJCAUVqF1YV2NjiXJQk0qLN7qHd4SY41tWiU1pjG+r24aVODMrbmdNUorf7oMYABFQx+8O9jVNW6eOSHFwz6AvMtIdJEBf2erhTjDRadEWQHo11v0dUCx3UOL+XVDrw+lVGeSoa7KhnlqcTrUymvdlDn8La4K9fceEDTNFSXC02jQ8YDAoFA0Ft4rFYOLVuO4nIz8RfLejQQBP9Gm0GvY3iimWiz0e+xpWkgQbTZyPBEMwa9jghT0/19nSzRStZ+A5rGebUDzSY9inKuodrCk0FF0c7rL0Dmow8x5MoFTZyoh1y5gMxHHmrx/ECfdcZotmcv4lx9WghYZ27Lvoo6Y3SL12g1RPGP1Llnr0dD1fzfAf6ROherIapD9RE7QsAozd+3Xy8Y+Aqk47ZllBagM/q9ABarkw/XH+Oelzby38+u456XNvLh+mNt6hO7ouGsO3wEyWDo8b/r3qKwvJ7Vm04wb3oaE0aLAvMt0ePBoM1mY/HixRQVFQHw+OOPs2DBApYsWcKSJUtYt24dAFu3buXqq69mwYIFvPLKKz09LMEAoS/F3NC1N/TutOsNujqnU8YlUWd3o2l+17l02xlkVNJthWc/lGjU2d1MGZfUYr+NFy2f3Y6mafjsNoS7p0AgCBV8djvf/vyXeCxVTPjZE5hHjerxPmflDMflVdDrZeKjTYxIiWLU0GhGpEQRH21Cr5dxeZXzNtomjklAaScYVDT/eS31F+BcMHjunJb66w6BPmNtlWgSGBQvYV4HRsWDJkGcrbL1a1Q14u0WNMCoeolQXISpXjQg3mZBUbXzrrGrBIzS2tJvtmSU1h39HnRv07uzGk7rnj3ETMxGNrRc17I/oWkav/vkIGEGHTd9J7v9BoOUHk0T3b9/P0899RQFBQUNx/Ly8njnnXdITk5uOOZyuXjiiSf461//ytChQ7n99tvZtGkTc+bM6cnhCQYAXS3G21UC2rYAzd/QAzTXtnW1XV/Q1Tk9vGEbl9Yfxu1VQINUexk2fTip9lKmVR4Aye+Kd3iDnoybFjV5zcD8uMrK8TnsKA4nSBKKw4mERP7//gVTSsr/Z+/cw6So7rz/qerrdE/3MPfBGe4XQZCLKCgGI1FUDIjZvDExauIlr242a2KyMe5md72QGJP11TWJySZGo9lovATDRVwWjRJDREERgUEuAgMzDHO/9XRPX6vq/aO6m57p6h6muqd7BuvzPP2MFnX6/OrUTJ065/y+3zMi2sfAwOCTiRQMsv+HD9HX0MDMf/1n3DNn5KTepQvGseX9BoIhSdO4JBhSNztfumBcv+M3Lp/J3se3IsmypomMmnKqnpeuvrhmMDoASlVfJsTqrGg5gjUcpKVkHAcmXMCM4+9R2dlARetRGidWJdW54lOT2XWojaneBuxSiGOOsbxTMoeLOvcwsa+JKb4GGgoqs+YemWiUlkq/qWWUple/B8kTtDHMZoFCs0gwJPHzl3ax+o7FmrKYoWg4/U3N+BtPUnX1cl3tM9J4e89JPvy4jTs+dy5jXMZWEqkY1pXBl156ifvuuy8+8PP7/Zw8eZLvf//7rFy5kp/97GfIssyePXuYMGEC48aNw2w2s3LlSv73f/93OEMzOEMYKHTXIibmzgaxB3rjy2tp2rQZEBDNZkCgadNmGl9eS8vm1+LbMGRaLh/obdPaj9uY1XmAC7tqme85iCKAjAlFgPmeg1zYVcuszgPs/TjZ+SzWPq1vvEmk14tgNiNarQhmMxGvl9Y3toyY9jEwMPjkIUciHHz4ETz7DzDtrm8Om85Li7IxBdx5nVqf1x8mEpHVzImIjNcfBuDO6+YnDQSmjy/m9mvnABCWZGRZRlHUn2FJfZbefu2cpE3SteoDdZUlXX3ZuEZnqJedNQvZNm0ZPY4Stk1bxs6ahTiDvZp1Hm/24HJYKIp4eat0Phsql9BmHcOGyiW8VTqforAPl8PC8WZPVuKMGaWBqslU01HVn7FBs5ZRWiZ7MGbqYDoUF9KunR+o8S7I3e/3cOEPRnhqfS2Tzypi+UUT8x3OiGZYVwYffPDBfv/f3t7OhRdeyH333YfL5eKOO+5gzZo1OBwOystPpY5VVFTQ0tIynKEZnCEMFLprkU7oPlT0CrL1lssHett0oAGAYLJhFQRQREQpSJdT2wAATrXPnu99H0WREQschNvbsZSVIfv7EESRWQ/cOyLax8DAYPjIlxlYOhRZ5vDPf0nXezuZ/Pe3U77k4pzHMHNSCTevmMWT62s51twbPz621MnNK2Yxc1KJZrnliycypaaIZzftp/ZoB5Ho5vFzp5Ry4/KZSQPBxPruuv48nt20n92H2wFo6ujj3CllacuB/ns4c1IJpvu/z4FN+2k82oEUjbX4nAs4f/lXNOvc+mEjpW47f5l2Bd3eEBY5zIrmt9lYeTG7i6ZzvHo2pYW2lO6eh+q74m0Tq2/25NRtEzNKqy4vpNcXotcfQpLVFNoihxWX0wqKommUFpNC7P72d1XdXsxuOyqFSGUmF3cwlZR4nXK0TleBWmc6B9OhGKV17fwA+1lnUTB2rGYso4kXXz9Ie0+A7910ASaTYZGSjpy6iY4bN45f/OIX8f+/6aabWLduHVdeeWU/dx8l6o5l8MlCTwcSE7qbzal/X9IJ3fWQ+EBXJIlgWxvW8nIGe6Dr7Qhyjd42ddrNeGTVAODy918gJvxPNAAIYsaRQshvr6rivF/9gt3f/i7hqGZQ6uvD4nQy77H/N2Lax8DAYHjYX9fJz1/aRViSsVtMuArUZ9Fr24+z5f0G7rxufspBz3BS//yLtP3lLcbfcD1jl1+Z8/oBNm07xhPr9iArYDEJ8e6jpauPh363g9uvncPyxRM1y04fX8zqOxYPqb7Ee+EqsNDjC1Fd5qSpw8djz3+Q8l5kcg8Ty1aXOTGZBCRJSVunLxDB1xfEF1BX4sb5W6kOtFHjb6XOWU23N0Q4IlHoSE4RTGxTkwBmUX3/3H24nb2Pb9Vs0yXzqnlt+3EKCywUu20Uu5O/1+uPcMUibfOVRP0ekgSiCUEU8R6tS7nthi8QwSwKtHX7VVM1AUyiev97fCE8fSHKxxTEVyb1IgUC9OytzdvveDZRTWOOcPkF4/PyzBht5HSofPDgQTZvPrXxp6IomM1mqqqqaEtYrm5ra+unKTQ489Erjh4odNci20J3OPVAD3t6kAJ+Ij09g26qm1huKJvx5hq9barXAABOieuPPfM7wj5fvF2kvj7CPh91T/93WnG9gYHB6CbfZmCpaH1zCydeWkPF5ZdR84XP57TuGIfqu3hinbotg8UkIooigqD+tERXPJ5Yt4dD9V1ZqW/gvYi5iFosprT3IpN7qLesJEnxgSDANF89oiIz3VcfP+YLSESk/n2a3jZdumAcFpOq09NiMD1lTL/nmjaVc+79V1zTpxLxeunc8Z7m+aBq7Vu7+gDVPVVdLIk5mKr3prWrL+ON1Dt3vIcSDlOy8IKMviffKIrCr9fuwW4z89XPag+wDfqT08Ggoij86Ec/oqenh3A4zIsvvsiyZcuYO3cudXV1HD9+HEmS2LhxI5dcckkuQzPII5l0IJk+mPUSe6CbbDZMBQ5Eu23QB3piuaF0BLlGb5vGysUMALrcFbw95xo63ZVYw0EqWo+mvBdxzeCbf0HyejFZLRScNRaT1YLk9dL6pqEZNDA4k8lUFzUc9Oyt5fAvfkXRnHOZ8vXb85ax9Oym/erqlYYJDKjHZUU9LxsMvBeKopB45anuRSb3UG/ZotZ6FnbVxj/j/c30mgsY72/ud7yotb5fOb1tqle/GWMo+r14nUV2ZIW0Dqayop6XCW1/+SvWsjLcKVYoRwt/+/Akuz9u56arZhimMadJTgeDM2bM4Pbbb+f666/ns5/9LDNnzmTFihXYbDZ+/OMfc+edd3L11VczefJkrrrqqlyGZpBHMulAMn0wD5XYClb3rg9xzzoHQRSxlpUhiCLuWefQvevDtCtYejqCXKO3TfUaAMApzaDZ4cDscmEbOxbRasM2dixmlwuz02HsM2hgcAaTazOwweg70ciBHz+MvaqSGffcHTX8yg+1RzswDTIONQnqedkgUaPW5Qni6QuhAPUtvXR5gkQkRfNeZHIP9ZaVIhLzew5xYVct5/UcAgRkwQQInBc9Pr/nEFKk/+RmJm06c1IJq+9YzBWLJiCIqn5eEAWuWDSB1XcsTpuWqGcPxvYePyZBUPeW1EBWFEyCQHuP/lXzUHc3Xbs+pPzTS+KxjUZ6+0I8sW4vU2uKuGrxpHyHM2rIydPtzTffjP/3DTfcwA033JB0zkUXXcSGDRtyEY7BCGMonYCWODr2YI7pDb3+CA67mSsWTci66UCiPXQkEOhnD+09Wofc10fL5tc07aFhaELufKK3TWdOKqH80dVs2dlAQ0K5idcsH/ReJGoGE+WGZofD0AwaGJzh5NoMLB1hj4f9P/gRgknknH//PubCZNOrXCLJCuZB3s8FASIZasZiDNSoJe7/nk6jlsk91Fv2uPMsXrAu48rWdykO9xIS1O8ICWbsSohmWymbKy6kx+LqVy7TNi0bU8AXLpuelS2rBiMYlqkottPaHYhuB3JqFCtHPTYqiu2EwvozZ9q3/g1kmYpLR/eWbk+ur6W3L8TqOy6Kp9AaDE7+proMDKLEOoF0TlmDvQR0egLsPdzOiVZv3BFs7+F25k4rTzsAGappTaauoLH63tlZx4KPXmfnOcu4aMGk0xq0xlzPDh5u5sqTW9l81hLOnlo1qLNbNhjKK0YmnaQecX0ikt/Pocd+zvS77jQGjwYGo4h8mIFpIUciHPjxwwQ7Opj9wwdGhIOxSRSixnqpz1EUsvbya7OYaGr3xjVpsqK61QiCgCm6x15rVx9jywr7lcvkHmZStsfiYt3YS7mlfiMCCgpC/OfasZcSFpP3zM20TXPpeht3MC1z4ukL4fWHkWUFURRwO6y4HVYArBnc/9Ytf8U5ZTKO8dmV1OSSnQdaePP9Br54+XQmnVWU73BGFaN3LdjgjMFpN+PzR2hs89LjC4GiOmXFZiEb27z4oitMWmzadox7Ht+q2l8r0dm+qCPYPY9vZdO2Y5rl9JrWxFxBIeYGSj9X0FQvD4n1lXWdpNLXQmn3yUHrG3iN1b5mqv1tVPe1DHqNetHbNpmSqabS89F+eg8exLP/wLDEZ2BgMDzkywxsIMee+W88+z5i6j/+A+4ZZw9rXafL7MmlSIPMyEmKel420KtRy+Qe6i0b8xWoDHaiCGCVwzikAFY5DIJ6PPG8GJm0aa77x1jbmM0iJW474ytdTBzrZnylixK3HbNZzOhvw3e8Ht+RI6N6VbAvEOYXa3ZTU1HIF5cN/2rtmYYxGDTIO+edXUFb1BwmlVNWW7efBWcnO8zqdQTL1LluqK6gA+ur6apDkGXGdR4btL6B1zjN14CIzDRvQ06c5HLp6pepprJj+w6UiETHu9uzHpuBgcHwkS8zsETa/rqVpldeZeyKq6m4dOSY2N24fCaiAFIKAy01dVA9LxskadQGDJpSadQyuYd6y952zSwApnobsEshmu1l/HHsZTTby7BLIab6GvqdF0Nvm+ajfxzuv42mVzchWq2Uj+LB4O//Zz/t3X6+ed18LObMXFU/iRiDQYNRg9Yknl5HsEyd64a6gvX2ui1Mb9jJuc0fMr1+JxXdjQSsTiq6TzC9Pnq8YSdvr9uSVPbV329mQUctCzv3saB9L+N8zfjMBYzzNbGgfa96vKOWV3+/WaPmoZNPV7+hiutjhj6xT8/eWiwlJfTsre133NiSwsBgZJNrM7CB+I4d5/Dj/4X7nJlMvOWrw1KHXqaPL+b2a+cAEJZkZFlGUdSfYUkdzNx+7ZysyQViGjWIDZbU3ldRlPjgSUujlsk91Fv28oUTuGR+NUURL2+VzmdD5RLabWPYULmEt0rnUxT2ccn8ai5f2H/fP71tmo/+cTj/NsK9vbRt+Qvln74Ei9s1eIERyL6jHby6rY4Vn5ps7CmoE0MzaJB3PjjYSvmYAjo8qcXR5WMK+OBgKzev6D+7p9cRLFPTmtgKVuUVlyOIItO/cxctr/2Z7j17NL+r9uM2FrTtxxoJEjZZkUUTsmhClCQmN+7FIoUImW28/3ElqwaUPX6ym8t7DmGXgoRMViRBRBZMKILM3O4DWKUQAZONP5/MTopQ3EkuIifpEwoLLLgd1rRtEyMX+r1EQx/R4ehn6NO0aTNyXx/mQmdKQx8DA4ORQy7NwBKJeH0ceOg/MDkKOPvuf8qrc2gqli+eSGmRnSfX19LU4YsfH1vq5GurZrNwVmpt41D1bTGNWmmRnfZuPzEPlVBExmwSKC2yYzGbNDVqMyeV8HdLp/LUhn00+/vixwsLLNx2zay0L+szJ5Vw1/Xn8eym/dQe7Yjr/2dPLk2rjb/7xvNZW1PE/2w6AJHoAFUQ2F86gwU3XsvXLp2mWS6xTXta2ril4VWeHvdZyirLU7ZprH8MBCN0eAIEglLc78xuM1Hqtp9W/zhUEtsmm74BLa+/gRwKMXbF8qzFmktCYYmfv/Qh5WMKuClLK+OfREbeE8/gE0fMQMZuTS2OTmUgo9cRLFPnuqG6gh51nEXfnFWc9/FfKPT3EIkK2iOiBasi0+Wq5INpn6aFZNe6OsdZ/GncMi5rfpeicC/hqFtaWDAjKGFaCsp4o/JCOszZmdXzBSJYRGjtDqAoCqIgqAN0BTy+EL19YSrG2ON9bioS9XvF583PSmwDydTQx8DAYGSRS5dGAEWWOfTYTwm2tTH7wdVYS4bXjEsv++s6eWbjPhQUJla5MJkEJEkhEJZ4ZuM+XA6r5kBrf10nP39pF2FJxm4x4SpQjVpe236cLe83cOd185PKLZlXzdq/HKG3L5T0fRFJobmjD5fDyucunZL07799ZR9r/3I46bjXH+anL35IfYuXW1fOSvr3gbFWlznj19jU4eOx5z/QjDVW7vXt9ZQUqQOxxLZ5fXs9MyaUpiwXa9OlUj1Oyc+lcj0fUJayTX2BCLIk0d4T7HdcAfxBiRNtPsqKbJhM2U1VTGybueYexofamWPp4ViHK23bpEORJJr/ZxPu2bNwTpyY1XhzxQuvH6SxzcsDt19Egc0Y0ujFSBM1yDunXMRSi6NTuYipjmDpv1/LESxWZzqy6VzntJvxWN28O+tq4nsmqNEBAu/MWo7H6k55jd0WF6/ULFVLKAqyov4EeKVmKd0WVxad5ERauwLRulU9ROwTS8dt7QpgtaR/fOjR77V3+/njG4f45iNbuO3B1/nmI1v44xuH0uov9Br6GBgYGDSu20DXezuZeOvNuGfOyHc4mujVqcXKRSSZUFjiZIeP4829nOzwEQpLRCRZs9yEKnd8IJjoISMIp/6/ty/EhCp3v3I79jXHB4KxcxM/AGv/cpgd+5qzfo2ZlhvfcQgBmND+cdpyJpH4QDDVNbb3BMnmVn2ZeA6ko3PHewTb2jlr5WezF2wOOdzQzctbDnPZBeM4T8NTwuD0MQaDBnknEwcyvY5guXaui9U3xtuGIoBFCmML92GVQigCFHvb0l+jrFDia0fhlFuaTQ6jACXediRZyaKTXAFSdEVQC1EQkBSFsqL+6UWZ6vcycWgbqqGPgYGBgffwEeqfe57SixYx9rMjN01Or05ty84GfIEw7T0BPFGn7sQsj/aeAL5AOKncxr8dRRSi05YJ/auiqB8BEAX1vESeXF8LkHK7htjx2HnZukY95XY88RyX7HuFpfs3snj3elx+D5Ig4vL3sHj3epbu38gl+15hxxPP9f9CRTita4ydlw0y8RxIhaIonPjTOmwVFZRccH7WYs0VgVCER/6wkzGFNm67Zna+wxn1GINBg7yTiVOWXkewXDvXxeqraDmCNRyky13B23OuodNdiTUcpKL1aMr6VnxqMrLS3y1tzVmX0RR1S5via0BW1POyQXtPADG6l5QWsqIgCup5icT0e40vr6Vp02ZAiGpvBJo2babx5bW0bH4NReNeZerQlumWFAYGBp8spECAg488hqXIzZRvfD3qYj0yGYrGPZEtOxvo9akGI6myPHp9yYPB2qMdmEQBi1lMyjhJPD5Qix/TMsYGjVqfxPOycY16yx070UV1zwnGdtVT1dUQTSYRURSo6mpgbFc91T0nqDsxwIk85qCaahJaGXBeFqj9uI0Zbfs5u34nkxv3ogCyaEIBJjfu5ez6ncxo28/ej0/PdRuge9eHeA99TM0X/g4hyymtueB3Gz/iRKuXb18/H1d0n0UD/RiDQYO8k4lTll5HsFw718Xqc4Z62VmzkG3TltHjKGHbtGXsrFmIM9ibsr7jzR5cDks/t7Q2a3+3NJfDwvFmT1ZiDYYlKoodgKrJVNNRlaiTnNrTVRQ7CA1YWY3p9wqnTkEUhX76PVEUKJw2hVkP3EvxvLlJdWbq0JbplhQGBgafLOqefJpAUxPTv/0tLK6R7aLoC0QwDeKUZjIJ9AX6a9w7PQEU0md5KCh0efpP7EmyEk17FDCbRMzRAaHVLGI2xQaVxPuDbKD3GvWW21k5j7fmrCJktgGgRNso9jNktvPWnFXsqpzXr5wkK5ij9UW7RpWEwa7ZJGS1bY46zmLrnFV0uSpAEPp5DiAIdLkq2TpnFcccZ53W9ymKQv3zL2IrL6Ni6aVZizNX7DzQwsa367hmyWTmTTfSQ7OBobbMMUN19fqkMHNSCTevmMWT62s51twbPz621MnNK9I7kC1fPJEpNUVxB7JI1IFs7pT0DmR6nctA332cOakE0/3f58Cm/TQm1Fd8zgWcv/wrKevb+mEjpW47f5l2Bd3eEBY5zIrmt9lYeTG7i6ZzvHo2pYW2lO5lh+q7hnSNMSe56vJCen0hev0hJBlEEYocVlxOKygKgoZGMabf2/3t70bziYR++r1UrqJxB1NJidcpR+t0Fah1pnNoG6qhj4GBwSeX9m3v0PL6n6n5P39H0bkjP8XslK4+9aBHS+MeCstRhbqCJCnIihJ/LIuCgMkkIKBuJZGIqsVXTqU8alSrpcXPBL3XmEm5Fms1r5//Za7e/kz/AgJsvuDL9JkKcGisjKKoHgcRSe43IBQEMJtEUJSst41HVj0HLn//BYj7l57yHAhiToo1FbFVwSn/cAeixZK1OHNBjzfIT1/YxbhKF1/57HbpvXYAACAASURBVDn5DueMwVgZzCGZaKLOdDZtO8ZDv9tBS1cfFpOA1SxgMQm0dPXx0O92sGnbsbTlp48vZvUdi/nTT1ay/uFr+NNPVrL6jsVpB3T76zp57PkPaOrwUV3mZNJYF9VlzrhzWar7ofc+6q3PF4jQ1t1Ht1cV9I/zt1IdaKPG3wpAtzdEW3df0swnqO16z+Nb2X24Xe3AREBR2H24nXse36rZrjF9o9kkUOy2RQ19XIyvdFHstmE2CWn1lHr0e75AhFBEprHNS09U22ISAQV6fCEa27yEIrLmNRoYGBicLsH2Do784lcUTpvKuOu/mO9wTgu9GnerxYQkK4TDsrpSFR0IotDvuHVAmmUqLX7iIS0tfiylfzC0ztN7jZmWq24/AoCoyIiyhKioA+NxbYc1y8U0/Go2kToki30UBSIROasa/sRY9XgODERRFBpeeEldFfzM0qzFmAsUReEXa3bT2xfiuzcswDZIerDB6WMMBnNEppqoM5lD9V08sU7dn89iEhFFEUFQf1pM6q/oE+v2cKi+K93XDIl8O5cN5f5LkoQvcKqzm+arR1Rkpvvq48d8AYmI1L9D1Nuumeop9ej3bBYTrV3qnlQmUYjqd2LaFnW2s7WrL+mlxcDAwOB0URSFI7/8L+RwmOn/dNeI3E9QC73P5CKnlegYECE2YkH9GfWRQVbU8xJJ1uJHC0ZHg6m0+M7THAxqnaf3GjMtN+7kRwiKgt/m5J3Zy/HbnAiKwrim/ZrlYhr+fm0a/SS2abY0/Imx6vEcGEj3rg/pPXiImi98ftStCr7xXgPv7G3ixqtmMrm6KN/hnFEYg8Eckakm6kzm2U37kRXigvaBmEQRWVHPyxa5di7L5P4XtdazsKs2/hnvb6bXXMB4f3O/40Wt9f3K6W3XTPWUevR7ZUV25JjTnQaiICAr6nkGBgYGemh766907dzFhJtuoGDs2HyHc9rofSZXljj6u4ImfBJdQStLHP3KDdTiK9HVMllJr8VXSB5YDiTVv+u9xkzLFUT6OFI6lVcW3MTJ0sm8suAmjpROpSDcp1lO1fCr15DKIMflsGZNw58Yqx7PgUQUSeLY736PraJi1K0KNnf4eGLdXmZNLuXaS6fmO5wzjtExLXYGMBTHq1xttjtSqD3awSD6b0wCSc5lmaBXo6b3PmZy/6WIxPyeQ9jkECHRiiSIyIIJFJnzeg5hlUMERSv/a+2vq8ykXWdOKmH1HYvjukivP4LDbuaKRRMG1bfq0e+19/gxCULUqTQ5aFlRMAlCVh3aDAwMPjmEunuoe/K3uGaczdirr8p3OENGj66+vcePSRRVzaCs9NsmQhCiWRhoP1eXL55IaZGdJ9fXxt0/JRnGljr42qrZLJyVvH+r027GYTVhs5po7w70c6QWBYGyMXYcNrOm3jx2jXp0/JmU673/Ydasr6Wp9ZTDaXDqMr62arZmm6oafhtFhVY6evwEglJcwWe3mSgtKsAsCll/l5s5qYTyR1ezZWcDDQl98sRrlp+250TLG2/Sd+w4Z9/9nWFdFcy2N4YkK/zn8x8gCPCd68/Lqh7TQMUYDOYIXyCCqyB9c5tMAl7/J08TJclRLVsaBAEiWXYuM4sCbd1+FEWdHY3qvunxhfD0hSgfU5DkCKb3PmZy/487z+IF6zKubH2X4nAvIUH9npBgxq6EaLaVsrniQnos/R3xMm3XsjEFfOGy6TmZnAiGZSqK7bR2B6IpSKce9rKiIAgCFcV2QmHtLUQMDAwM0lH3m6eQ/AGm/uM/jEor/U3bjvHEuj3IClhMQtybK6arv/3aOSxfPLFfmWBYpqjQmrQNEET1bZJCWZFN87m6v66TZzbuQ0FhXEUhDa1eSlw2FBSe2bgPl8OaNFhaMq+ajX+ro7cvhCgKmIVTccoKdPUGiUgKKz41SfMa99d18vOXdhGWZKrLnJhMApKkxHX1d143X3OAprecnjaN9eVms0h1eaHmdSiKMizvcpn0yZG+PuqffR7XzBmUXrw467HFSLwXdosJV4Fq8PPa9uNseb8h5b1Ix/OvHeCjuk6+ff15VAxYxTbIDkaaaI6IOV6lQ8vx6pOA6lyW/pxsO5fp1ajpvY+Z3v8ei4t1Yy9VY4wKN1RTcIG1Yy9NGgjGrivX7aoXp92MxWyiusyJ22mF2D6HAridVqrLnFjMpk/k34eBgUFmdGzfQfvf3mbcF7+AY1xNvsMZMnr13zaLiW5vELNJiK4Cqgioz32zSaDbG0zq5wZq3K0WtQ4F0mrc504rx+MLokTdNAf2q4qi4PEFmTutPOkac63H19umo/Vd7sSaPxHu6WHSbbcM256aw+GN8cHBVl768yEuu2Acnzk/O/s+GyRjDAZzhF7Hq08CqZzLEtFyLssEvRq1XDuewanOtzLYiSKAVQ7jkAJY5TAI6vHE82Jk2q7t3X7++MYhvvnIFm578HW++cgW/vjGoWExOYo7mJpFStz2qIOpm/GVLkrcdsxm8RP792FgYKCfiNfLgR/9BHNhIdV/d22+w9GFbv13tJ8zier+gFaLCZvFhNViwmwS4+UG9nMDNe5CdBuKSFQvmErjvvvjNtxOK4IgIMmqdi/2kWQZQRBwO63s1tgcPdd6fL1tOhrf5QItrZzcsJHySy/BNW349HbZ9sbo6PHz6B92Mq7Sxd//3ZxshmowAGMwmCMydWg8k0l2LutPKueyTEjUqGmRSqOWa8czgNuumQXAVG8DdilEs72MP469jGZ7GXYpxFRfQ7/zYmTSrrneBsX4+zAwMBgO6p9/EYAp3/j7UeMeOhC9+m+9/ZyWxt0siv1WxGIa94Hl3A5r2gwPt8OaVC6xzkhEptMToL6ll2NNHupbeun0BIhE5JR1nq4ePxG9bToa+6pjz/w3giAw4aYbh7UevfdCC0mSefjZnQRDEv/8lQuwW0fn3+5owRgM5ohMHRrPZAY6l8my6l4my+mdyzIhplEDNGcwAU2NWq4dzwAuXziBS+ZXUxTx8lbpfDZULqHdNoYNlUt4q3Q+RWEfl8yv5vKFE/qV09uu+dgGxfj7MDAwyDZ9J07QvGkzlcsup2zxRfkORzeSnLABfAoEgSSNu95+zheIYBowUjInrAyCqnEfuO9rrFy6DA+tcrGy4YhEY7sPT3SvWTG6V4PHF6Kx3Uc4IqWsMx1adept09HWV3W+v5OObe9Q84XPYyvLXnaVFnrvhRbP/u8B9h3t4Bv/Zy7jKpNlMAbZxRhq55BMHBrPdJYvnsiUmqK4G1gk6gY2d0p6NzC9OO3qSld1mRNPXwivP4wsK4iigNthxR21jrZq6On03sdM7v/dN57Pn6eX8+cN+yDa4SAIHKmaxaXXXMfXBgwEY+hp11iqR6oNhG1WE15/mC07G7Lulmb8fRgYGGQDRVGoe+oZRLuN8Td+Od/hZERMb5du8KKl/471c5XFBXR4AsnOl267mj6qUU5dBVTi/WM4usl6pycQ7x9TaePN5tSBptLT2Swize19ICSnbsZWN1u7AlSV9TcQiccqkNIZHCW5Tr1tCvnrq4bq0in5/Rz91RMUjKuh+nOrhiWmRDK5/4m8v7+FNW9+zJUXTuDSEbTCeiZjDAZzTC4dGkcbJW47504to9sbjD/ozp1aRol78L3lhvqQXDKvmte2H8dsEvEHI4TDstpJSgp+MYLDbiEiyVyxSHuQdbSxh9e318cttwECQYkJVe7T7giG6o06b3oFf7c0mHSN86ZXpC0Xa1d/ZyfLdr7E6wu+mLZdY6kegWAk5QvEcG+DYpZCLP34dXaes2xYvt/AwODMpuv9nXR/sItJt92Cdczo3qB69uRSdh9uT5vKJSkwd0r/lR/V3fMovX1hFEXBkmAvHY7ItHT5cTksSRukDywnCgKiICApCj3eIL194ZTlXtt+nMI0NtaBsKTZr5YVFdDY5sOSQsMnCgJhWaasqH//muhgmsoZ3OWwJjmY6m3TeLw5fpfT49J5/LkXCLa1c+6PH8zJBvOZ3P8YbV2qTnDSWW7+77XnDkeYBhoYaaIGI4JMNGp6yi5dMI5gKMKJVi/+6GAH1AGaPyhxotVLMBTRzPv/7Sv7+MFvt/cbCAI0dfj4wW+389tX9o2IaxxYbkrjPgrCfUw+uS9tOV8ggtcf4kSbT7tt2nx4/aHTSvUYComxlnWdpNLXQmn3yWHTKRoYGJyZyOEwdU8+TUFNDVWjcE/BgSTqvyOSTCgiEQxLhCISEUlOqf9W3T1DUXdPNd0/9jGJYtTdM5Tk7pmqHICYplwmerr2ngBiTGOogboHLUnbZOh1MM2HV4Fe9Eg3ej8+TNOr/0PV8itxz5yRkzgz1VNGJJn/+P17RCSFe75yAbZB9IcG2cMYDBrknUw0arGyEUkmFJY42eHjeHMvJzt8hMJqR6lVttMTwOMLx/9fSPjE8PjCdHr6dzw79jWz9i+H1TJC8gdg7V8Os2Nfc9avcahlB5Yb33EIAZjQ/nHaciYR2nuCaa+xvSdIiglcXQyMtaarDkGWGdd5bNh0igYGBmcmTRv/h0BzM5O+dsuoNY1JZPr4YlYumUJE6r95vKKomraIpLByyZSktH/V3dMWdfdUtYIQ0wwqUXdPW5K7p1Y5QVArTVcuEz1dMCxRUeyI1zEwVoCKYgehAU6eeh1M8+FVoJehunTKkQhHfvFfWIqKmHDTDTmLM1M95ZPrazlwvIs7vzAv5R6OBsPD6H9KGox6MtGobdnZgC8QTkpniYnOBUHA5bAklX12034UwGoWkWQFWVE7WEFQ9QkmUSAiKzy7aT+r7zi1QeuT62sBUuoMYpvWPrm+loWzqrJ2jXrK7njiOS7ZVxvXPLj8HiRBxOXvYfHu9YDa6e54oo6rv/e1U1+oCKd1jbHzssHb67YwvaE2PhNY0d1IwOqkovsE0+t3AurLwtvrgqy6+eqs1WtgYDCyGGrK/0AifX2ceHktY86bT/H8eTmIeGjoub72bj/vf9RMxRg7vf5wUuq+q8DC+x81c82Syf2+Q3X3tOCwm+noCRAIRhLKmSktsmMWhaSU/1g5m8VEe7efUCRh9UxRUzptFlFTKjBzUgk3r5jFk+trOdbcGz8+ttTJzStmpdxwPK5vLHGkjVUYoOGLOZhazSLtPQGUUJAVLW+zsfJisFgpdduwW82asS5fPBGLWeCpDfvigxVQJyRvu2ZWkinbwHuSye/pUBiKS+cXLptO49r1+OqOMeOf78bsdGZcv+T3c+ixnzP9rjsxFaS/Nr16ys3vHuPVt+u49tNTWDJ/5GzL8UnBGAwa5J2hPugS2bKzgV5fOK3ovNeXPFCK2UoLgrrxrhYmQUmylY6lhg62mfvAFNJMrlFv2WMnuji350R8c3oFAUQRRZap6mqIH99zov+DN24zHuuJB6IMOC8L1H7cxoK2/VgjQcImK7JoQhZNiJLE5Ma9WKQQIbON9z+uZPhl8AYGucHr9fKlL32JX/3qV9TU1PDiiy/y+9//HkEQmD17Ng888ABWq5XHH3+cl19+GbfbDcB1113HDTfkbsY/V+jRRQ2k6ZVXifT2MuGG63MU9emj9/piE4LuQhvuQpvmd2tNCPoCEcyiQFu3H0UBi1mMT+aFIzItnX2UjylIcsz0BSLIkhxPyYx1AwpqumZTh4+yIjsmU3J6yKZtx3hi3R5kBSwmIV5fS1cfD/1uB7dfO4fliycmlRuo/dOKVUv7p8YqxbNZJvtbqQ60UeNvpU6sprnTT1mRDZMpuQ9NFWtfMMIv1uwmHFE0Y83G7+lQ8AUiuArSv66bTAJefwTv4SM0PP8ipRcvpuTCRVmp3/PRfnoPHsSz/wDF580f9Pyh6in3He3gV3/aw3lnV3DzilmDFzDIOkaaqEHeycSOuNMTQEFJu3m8gkLXgHRPvbbSesnkGvWW3Vk5j7fmrCJkVl8elOgFx36GzHbemrOKXZX9Z88lWYkPkKOZOirKqUGw2SRkrW0AjjrOYuucVXS5KkAQiIjqKmhEtIAg0OWqZOucVRxznJW1Og0M8snu3bu5/vrrOXbsGAB1dXU89dRTvPDCC2zYsAFZlvnDH/4AQG1tLY8++ijr169n/fr1Z+RAMBtb2oR7e2lct4GSCxdROHVKTuI+XTK5Pr37t9ksJlq7+gA09XQArV19WJP2FBRODQS1NBSo2r2BTpuH6rt4Yt0eACwmEVEUEQT1pyU6cHxi3R4O1Xclxa9X+zdQ1jDNV4+oyEz31aeVNeiNNR9bL51yd02NJCm4LAqH/vOnWMYUMeXrt8d1npnSsX0HSkSi493tWfm+RFq7+vjx796jotjB3Tcu0HRvNRh+jMGgQd453Qedlh1xKCwjAAqKprBeXftS91tKRO1c0seVylZaD5lco96yTruZFnc1r5//5eQVPgE2X/BlWtzVmpbbAkQ7OXUsqCjRhUIhepzstU0sVo/VzbuzrlaD62dbI/DOrOV4rO5BLakNDEYLL730Evfddx8VFaobsNVq5b777qOwsBBBEJg+fTonT54E1MHgr3/9a1auXMnq1asJBoP5DH1YGKouSouT6zYg+f2Mv/6LwxWmbjK5Pr0TgmVFduTYfn0aiIKArKjnJRLrGwcbSwzsQ5/dtB9ZSc7SiccoisiKet5A9Gr/KrsbWdhVy6KuWhZ21jLB30yvuYAJ/mYWdkaPd9VS2d1/oKw31mz8ng6VJfOqCYS1TVliBMISV/XuwX+ikWnf/EcsLv1783Xv2cuJP62Lf3r21mIpKaFnb22/49179uquAyAQivDg0zsIRST+7dZFFEa3LDHIPcZg0CDvnO6Dbsm85Dxyq8WEJCuEw7K6UhXV/REV1seOD5z5nD25lEHGV0iKel4iqXR7Axl4XibXqLdsrFx1+xEAREVGlCVERR0Yj2s7rFlu9uRS1ZQguq9U4sSwokAkorbpwLbJhFisY7xtKAJYpDC2cB9WKYQiQLG3LWX7GBiMRh588EHOP//8+P9XV1dz8cUXA9DZ2clzzz3HZZddhs/nY+bMmdx9992sXbsWj8fDL3/5y3yFPWzoXf2KEeru4eQrr1K25GKcE1NrvfJFJtend0KwvcePKTq4SuVCahKEpJT/tm51NXGwCdPYeTFi8ot0mASS5BdwSvtXWeLAYhYJR2RCEZlwRMZiFqksceB2WJPap9cbYH7PIRZ11TK/5xAKArJgQkHod7zX238CRW+smf6e6uF0XDrHe05QuOcdxq5cwZh5czOqT5EkWja/RuPLa2natBkQokZMAk2bNtP48lpaNr+GksKJ9bTqUBR+9uKH1J3s4e4bzzc2ls8zxmDQIO9kYkdc5LQiJ6xaxVfAos6XqsZBPS8RvbbSztMcDA48L5Nr1Fs2Vm7cyY8QFAW/zck7s5fjtzkRFIVxTfs1y6341OTkNo1+Ett04B5TmRCLtaLlCNZwkC53BW/PuYZOdyXWcJCK1qNpLakNDM4UWlpa+OpXv8rnP/95Fi1ahNPp5De/+Q1TpkzBbDZz66238tZbb+U7zKyTSSo9wMn1G5DDYcZ98brhCC9jMrk+vROCwbBMUaElrQtpUaGF0IDMGVkBs+nUBGDsk4jZpJ6XSCbyC18gQiiqDQxH1D0RbRYxOjBUaOnsIxSRk9qnznEWa8ZdQYutFAUICeqAOCSYUYBmWylrxl1BnWNsVmLN9PdUD4O5dNrCfi5r3Ipj/DgmfiXzFPLi+fOY9cC9FE6dgigKiHZ15Vi02xFFgcJpU5j1wL0UZzDoXPPmx2z9sJGvXH0O58+szDhmg8wwBoMGeScTO+LKEgeicKrTIuETW9USBfW8RPTaSiskDywHovXvmVyj3rKxcgWRPo6UTuWVBTdxsnQyryy4iSOlUykI92mWO97swRVN10h8EUh8IXA5rBxv9qRth6EQi9UZ6mVnzUK2TVtGj6OEbdOWsbNmIc5gb1pLagODM4EjR47wpS99ic997nN84xvfAODkyZOsWbMmfo6iKJjPgO0SBpJJKn2kz0/z5tcovWgRjpqRmT2QyfXpnRC0WUR6vGHMJiGe/g/E0/zNJoEebxirZYD5miggIGAxi/3KxVD7XCFJKpCJ/EKvvtEkCnisLjaO+wwIAkJUYiBE04ReHf8ZPFZXyljTSUy0Ys3kPmZCzKXzikUTEETVLEYQBa5YOI6vhj6APh/Tvv0tRGt2Ui3tVVWcfc/dgEC/WQQEZtxzN/aqqnTF07Ljo2Z+v2k/l8yv5vNLp2YlXoPMMAaDBiOCmB21gMCx5l6ONHo41tyLgJDWjrq9x49JFDHFnMA49REEdYbOJIqazpfLF0/kX766kMpiB2FJIRRRCEsKlcUO/uWrCzVdxJx2M8UuGxXFBUk6DFEQqCguoNhl0+wIZk4q4a7rz2NsqZPGdh91Tb00tvsYW+rkruvPS+s+prfszEkljLn/Yd6euoxjrT61XVt9vD11GWPu/w/Ncls/bKTUbaOmopACm6nfC0SBzURNRSGlbltW02BisV7x6GomXrMcwSSqnZ1JZOI1y7ni0Qey6s6WKe3dfv74xiG++cgWbnvwdb75yBb++MYhYx9EA914vV5uu+02vvWtb3HrrbfGj9vtdh5++GEaGhpQFIXnnnuOZcuW5THS4SGTVPrWN95A8vVRveqa4QovYzK5Pt0TgkUFSNGN480mEavFhM1iwmoxYTaJmEQRSVEoK+pfLiajUN22T5WzWU71B1oyCr3yCzVWffrGWJ2VwU4UBOxKBKcUwK6EAYHKQGfqWE9DYjKwXCb3MVNiLp0/+6elPPmvy/jZPy3lws5afLt3M+m2WyicPGnwLxkCvrqjIApI/j4inh6kvj4QBLxH63R/58cNXfzH799nSnURd143L2smNwaZYQwGDUYEm7Yd46Hf7aClqw+LScBqFrCYhLgd9aZtxzTLqWkwViKSkjQjqShE02CsSWkwoNpDP7NxHwoKE6tcTKl2M7HKhYLCMxv3sb+uM6nMknnVePrCdPUGEUUBazSVxWoWEUWBrt4gnr6wZkewv66Tx57/gKYOH9VlTiaNdVFd5qSpw8djz3+gWV+mZfW0aywNxm41UV1eyJSaIqbWFDGlpojq8kLsVlPW02BiaHV2X7hs+ohaEdxf18m9v97Ga9uPo8gKrgJ1f6zXth/n3l9vS3sfDQxSsWbNGtrb23n66adZtWoVq1at4qc//SklJSWsXr2ar3/961x11VUoisItt9yS73Czjt7VL0WSOLnhVVwzZ+A6+/Ss7PNBJlIBSLMytGgCq+9YrDlZ1t4TQBTU7SC0kBUFUSDuHBojnYxCiA7KBJQkGYVe+YUaqz++HVSqWLX0jbE6J3mOUyAHaS4oY934ZTQXlGOXg0z01GvWqSmHgEHlEJnex2zSvXsP9c+/SNklS6hafmXWv79z+/tEer24pk3lnHv/Fdf0qUS8Xjp3vKfr+5o7fKx+cjtFhTbuve1C7NYzL8NhtGIMBg3yTiZ21DaLiW5vMG0aTLc3mJRaotceWq/9dSZ21HrL6m3XfKXBjAbyYStucGbz5ptvUlNTw80330xtbW18+4j169fzrW99C4Arr7ySjRs3snnzZh566CGsWUoFG0noXf3qeHc7wdbWEb0qCJlJBRK/YyiTZcGwREWxKpFQNYNq3ozq0Kk+4yuKHYQGrHSlk1Eo0TTML14+I0lGoVd+ocYqU1Fsj8aa7CaqxmpPmtiN1ekO+/hr6XxerVpCu7WIV6uW8NfS+bjDXs06VTmE+sxOLYewJMkhsnEfs0Gwo4NDj/wnBWedxdR/uGNYVtiCbW1MuPHLTP/OXTjGj2f6d+5iwo1fJtjWNnjhAXh8Ie7/zTtIssz9X7uQYrd98EIGOcMYDBrknUzsqGOpJenSYLRSS/TaQ+u1v87EjlpvWb3tms80mJFOPmzFDQw+KehZ/Tq5/hXsVVWULDxf4xtHFnquL5Ghpqc77WasZpGyogIEIBSRCYZVl04BNY3UahY1J/ZSyShK3eogp7xY+2Vej/wiFqvFbKK6zInbaYXYiqYAbqeV6jInFrMpZayzv/89mifNJyyj1ilD86T5nPv972nWqcoh7NSUO7XlEOVOSt12TTlEpvcxU+RIhIMPP4oUDDHjn+/GVDA8A88Z93yXqquuQIi+QwiiSNVVVzDje98d0vcEwxI//O12Wrv8/Nutiwzn0BHIJ29q32DEkYkddWJqiZbWIFVqyVDsob9w2anUo5j9tdthxdMXwusPI8sKoijEj8fOG1hOT32ZlNXbrksXjGPL+w0EQ5LmoCeXaTAjjUzuo4GBweDEVr9O5+/He7SO3oOHmPS1WxFM6f8uRwpDub5E9td18vOXdhGWZOwWE64CNYPjte3H2fJ+A3deNz9pELJkXjXr3jqCxxcC+m83G5Zkmjp8uB1Wrr10imZ9iTIKk0lAkhT8oQiiANv2NnH5wuQtPFKVC4Qlntm4D5fDqjlYWjKvmte2H6ewwEKJ206JxsqR1x/mikXZqdMXiOAqMGM2m6kuL9Rsc0VR8Pq15RB672M2OPbM7+ndf4Dp//RtHONqcl7/UJBkhUee28mB453c85ULOGdS9rakMsgexsqgQd7JxI5ab2qJXnvoWDmzWaTEbWd8pYuJY92Mr3RR4rZjNotpy0UiMp2eAPUtvRxr8lDf0kunJ0AkIqfU4emNVW+7jpQ0mJFIPmzFDQwMtGl9400Es5nySy/JdyjDit709AlV7lMDQY1tggA8fSEmVLlPuz6Xw4rNYuKDA61J9cXKxQxYTnb4ON7s5WSHL27IkiqNXq8WT2/bjFY5RMuf36DplY2M/ezVlF/yqXyHkxZFUXhy/V7e2dvE11bN5uI5Z+U7JIMUGINBg7yTiR213tQSvR1BJuV8/giN7T61c465pilqLn1juw+fP6LZ8eitM5N2zXcazEhltL5AGBicacjhMG1v/ZWSRQuxuM7stDO96ekbkNW82wAAIABJREFU/3YUIWHrpYEfAXVQuPFvR4dUn9NhQZIVNmw9klTOF4jQ1u2nJ9rPmURAgR5fiLZuP75ARDONXu8kpN62GY1yiJ59+zjyX09QNHcOk267Od/hDMq6t46w8W91XPvpKVyzJHn12WDkYLyxGOSd2ZNL2X24Pe3MhKTA3CnJ6QWx1JJ0qXuBsJSUWhIvB0npnoUFFtwOa9pyhebU0WqVO+/sCta9dQRRFJI0fLE017ZuP9d+OvmBGY9VEOj1hej1h5BlEEVwFVhxObVjzaRdIT9pMO3dfrbsbGDrh434AhGcdjNL5lWzdMG4EbESqff+GxgYZJfOHarTYeXln8lL/bl8VmUiFTBHN+KNRGLWLyoCYDarE5IDpQKD1eewqa+Of93VyK0rZ8ePb9l5Ao8v2G9vQIhu8xSdqPX4gmzZeUKzX4ltofTspv3UHu1AklWjttmTS7lx+UxN4xm9bTPa5BD+pmYOPPQw9soKZnzvuyM+LfrPO+r57Sv7+NTcs7hlxax8h2MwCMbKoEHeycSOeumCcciywok2r+aK24k2L7KsJD3QMyk3nLbSWgmI8VhbezVnW0+09mrGmkm75oPRsGXDSLIVNzD4JNP6xptYS0sYM3dOzuvO9bMqE6mAwqmBYGwlMLqdXvz4QKnAYPXFNqP39oX7He/0BFAG2StQUdTztNCzhZLethlNcoiIz8f+Hz4EisLMf/8+5kJnvkNKy9Zdjfz8pV3Mn17Ot68/D1Ej+8hgZGEMBg3yTswaWgFCEYlg+NQnFJFQSG1H3R/1gXOqHzrdB9Dpl9PbgXxwsJXy6LFUFt/lYwrYebB1kFjVc0+lf6ZOWczE5jvXjJYtG0bTC4SBwZlKqKuLrl0fUrH00pyvkOTjWZWJVCASLae1lx6oe/EOlAoMVp8gCBTYzIQiEuHIqYmxUFhCFFStWESSCcX68bBERFKflaJA0lYWkB/t32iQQyiSxMGHHyXQ1MTZ93yXgrFj8x1SWrbXNvHIH3Yyc1Ip379lYdK2XgYjE2MwaDAimDjWTanLhmXADJ/FJFDqsjFxrFuz3JadDYiiQE2Fi6KoZlCSAQGKnFZqKlyIopCkF9BbDvR1IL5ABGeB6lqmVV91eSHOArOm8Ug81vJCTV1kTXlhyliXL57IT/5xCXOnloEgEJEBQWDu1DJ+8o9LUtp855rRtGXDaHiBMDA4k+nc/h7IMmWXLMl53fl4VunVt5UVRSelUs1vCgPOG0J9ZrOIosDew6dSTK0WEVlRCEfk+KokEF99DEdkZEXBZkl+9cyX9m+oezfmEkVROPqbp+je9SGT//7/MmbOufkOKS0fHGzlx//9PlNqirj3tkXGpvKjCONO5ZiRronKBkO9xtiMoM1qZnyVLenfgyGJn7+0i9V3LE4qn8kWEXaLiYgk0xeMEA7LahqNBH1ihAK7Oe0WAZ2eAHsPt3Oi1RvXNew93M7caeWa1xifvUxj75lq9jIxVn8wAsEgK1ve5tXKi/GLAg5b+lj1Mho0MdlC8vs59NjPmX7Xnae1Z1M+bcUNDD7pdO7Ygb2qCsf43Kdj5+NZFdO39QUiBENSkm7cZjVpp6cLpzJJtLqeeIaJ0H9l7XT0dE67mb5AmO37mjhvRgWgTk76olsxpKpPksHltCb9W6bavyG3zSig4cU/0rxpM9WfW0XVFcvyHU5aao+08+DTOxhf6eKB/3sRDrsl3yEZDAFjZTCHjAZNVKboucZMZlp9gQihiExjm1dTT9fY5iUUkTW3evD6w5xo9aoDLE5NnvqDEU60evH6w5ordZu2HeOex7ey+3A7KApmEVAUdh9u557Ht7Jp27GkMkvmVePpC6eN09MX1py9VGMNcaLNhz8oUeNvpTrQRrW/FX9Q4kSbD68/lLVYYfRoYrKF56P99B48iGf/gWH5fgMDg+wQ6fPTvXsvJYsuQBhs75xhIB/PqrIxBaxcMoXWrj66vEEUWUEUFBRZocsbpLWrj5VLpiRN0kkylBWpE6xabqKg/vtAWfnppMN/64vncf7MSrbtbYpLHapKnIgJ7qUkfGLupaKgnjeQTLR/etpmpNP8v6/R8PyLVHzmUiZ89aZ8h5OWg8c7Wf3Uu1SWFLD6josodCQP9g1GNsM+GPR6vaxYsYITJ04A8OKLL7JixQpWrlzJv/zLvxAKqXvgPP744yxdupRVq1axatUqnnvuueEOLaeMFk1UJui9xqHMCA7EZjHR2tUHqPoIISqMSHQza+3qS8pbN4sC7T2qiD3V/kvtPYEkLcWh+i6eWLcHAItJRBRFBEH9aTGpf05PrNvDofqufuXmTivH4wuiKIpmnErUZW3utPKkazSJ0N4TjMc6zVePqMhM99UnxBpkgEmp7lhHkyYmW3Rs34ESkeh4d/uwfL+BgUF26N61CyUSoWTRBXmpP9NnVXu3nz++cYhvPrKF2x58nW8+soU/vnEo7fO0vdvPK1uPUOKyYbOIRCSFUEQhIqkplyUuG69sPaKppysssFI+xp60UicIUD7GTmGBNaWe7uYVsxAQONbcy5FGD8eaexEQuHnFLGZOKuGSeTV09wapPdKuxtkTQBSFuGFI4ngQiP9brO8dGKskqdfU5QlG9+Ltpb6lly5PkIikaLZrrG3KxxQwptCKIArICgiiwJhCK+VjCjTbZiTT8c67HPn1byg+fwFTvvH1vEx6nC5HG3u47zfvMqbQzg/uWExRYXJ2l8HIZ1gHg7t37+b666/n2LFjANTV1fHUU0/xwgsvsGHDBmRZ5g9/+AMAtbW1PProo6xfv57169dzww03DGdoOWc0aaL0ovcaM5lpLSuyIw/iXiYr6nmJxGZFUz1jY8cH7tP37Kb9yApJ20PE4xRFZEU9L5HdH7fhdloRBAFJVmdZYx9JlhEEAbfTyu6P25K+s7K7kYVdtSzqqmVhZy0T/M30mguY4G9mYWf0eFctld39B8t6Yx1Nmhi9dO/Zy4k/rYt/evbWYikpoWdvbb/j3Xv2ZqU+AwOD7NC5/T3MLhfuGTPyUn8mzyq9GRfq/n1hurwhwhEZi1nEahaxmEXCEZkubwhfIKypp+vwBGjrDiT1ZYoCbd0BOjwBzVg3bTvGQ7/bQUtXHxaTgNUsYDEJtHT18dDvdrBp2zEWzKygwGbir7vUvicYlrBZTEnupDEkWcFmMWkayOjNnon1V84CCyVuO+MrXUwc62Z8pYsStx1ngWVUvVv17K3l4P/7T1zTpnL29/4J0Txy1VwfN3Txb796mwKbmR/+/WJKi0bX6qvBKYZ1MPjSSy9x3333UVGh5pNbrVbuu+8+CgsLEQSB6dOnc/LkSUAdDP76179m5cqVrF69mmAwOJyh5ZxMVr9GC3qvMZOZ1vYeP6boACsiyXE30lBEdS+TZBmTINDe039WsK1bXU1MtSl77HjsvBi1RzsYZNyKSdDet8ntsFJZ4oh34KGIHO/YK0scuB1Wzfvf6w0wv+cQi7pqmd9zCAUBWTChIPQ73uvt/zeTSay5/l3N9ZYNiiTRsvk1Gl9eS9OmzYAQ7XQFmjZtpvHltbRsfg0lxbYcBgYGuUeRZbo+2EXx+Qvyts+a3mdVJhkXW3Y20OtT0zNNolou9olN9vX6kgeDE6rc9Ea3fxCE5A9Ab1+YCVX9DdpON6ukvrmXRbPH8vbuRoJhCQF1cjcdqf5db/bMmfRu5T1ax/4f/QR7VRUz/+37mGwjd5Xto7oO/u1X2yiwW3joHy6mosSR75AMMmBYB4MPPvgg559/fvz/q6urufjiiwHo7Ozkueee47LLLsPn8zFz5kzuvvtu1q5di8fj4Ze//OVwhpZz8q2JygV6rzGTmdZgWKao0KKmkMhKfBCnCtXVlJOiQguhcP+XelkBs+mUtmHgR0D994ETnJKspPOAAdROVmvfplBEpqWzj3BEwWIWsVliM7sKLZ19mtpGgDrHWawZdwUttlJ1+w1BHRSHBDMK0GwrZc24K6hz9LecziTWfGhicrllQ/H8ecx64F4Kp05RU5fs6sqxaLcjigKF06Yw64F7KZ43Nyv1GRgYZI7/RCOR3l6KZudvE2u9z6pMMi46PQEUlPT796HQNWD/vo1/O5qs4YMkDd/Gvx3tV24oWSWXXzAeXyDCO3tO4vOHNc8fiNZ5erNnzpR3K9/xevbdtxpTQQGz7v93LG5XvkNKye5Dbdz7xDsUu2z85Bufoqp0ZO97aDA4eTGQaWlp4atf/Sqf//znWbRoEU6nk9/85jdMmTIFs9nMrbfeyltvvZWP0IaNfGuicoHea8xkVchmEenxhjGb1NnDhG2UMIkCZpNAjzeMdYCVtXquEN88d2A5i1lEQEjSDKozlGkvEUUhqZxebWPsfI/VxcZxnwFBQIj26AKqRdyr4z+Dx+pKGauCorlqqqBoxpqv39Vcb9lgr6ri7HvuBgT6zSIgMOOeu7FXVWW1PgMDg8zw7FdT2t3n5CdFNMbMSSXcdf15jC110tjuo66pl8Z2H2NLndx1/Xmaz6pMVrBCYXnQXXMF1MnRRGqPdqj9mUXt5+KPOoF+xwdmhwwlq+TcKWVUlTp4fUd9fDA8GFrn6c2eORPerfoaTrDv3+9HNJuZ/cP7sZWX5TuklLz3UTMPPPUuVSUOHvrGp0adMY+BNjkfDB45coQvfelLfO5zn+Mb3/gGACdPnmTNmjXxcxRFwTyC86T1kGtNVD7QvRdSBqtCZUUFSIqCSRQxm0SsFhM2iwmrxYTZJGISRSRFSdpHafbkUtSdHgTNcoIgICnqeVrl0qFVTq+2MbHOymAnCgJ2JYJTCmBXwoBAZaAzdayyQjis7vlEzF48umoaOz6wXD5/V3O955Ov7iiIApK/j4inB6mvDwQB79G6YanPwMBAP56PDmAZMwZ7njfe3l/XyWPPf0BTh4/qMieTxrqoLnPS1OHjsec/0NT+ZbKCZbWYkrJUBiIrJE0mxrJDBKL9nDnaz5mj/RyCZnbIULJKRFHg8oXj2XO4PX2BQdCbPTPa3636TjRS++/3gSgw6wf3U3DWWfkOKSV/293Ig0/vYMJYNz/6h09R7Ep+XzEYneR0MOj1erntttv41re+xa233ho/brfbefjhh2loaEBRFJ577jmWLRvZe6oMlVxrovJBJteod1WovSeAGNuEXQNZURAFktzLblw+E1EAKYUmTJJlREE9LxvlYtrGdHFqaRsT65zkOU6BHKS5oIx145fRXFCOXQ4y0VOvWeeKT00mOgY85ZgKccdUBfUFYsWnJvcr90n4XY3Ruf19Ir1eXNOmcs69/4pr+lQiXi+dO97Ld2gGBgYD8Ozfj3vmjLy6K+rV/mWyglXiVt1A0/UfgqCel0g8O0SJZoeEo9kh4Wh2iKKdHTLUrJIrFk3AbMrsdVJv9sxo7q/8TU3s+/f7QZaZvfp+HDUjc8AK8Ob79Tz8+/eZPr6YH96xGLfGXpEGo5ecDgbXrFlDe3s7Tz/9dHwLiZ/+9KeUlJSwevVqvv71r3PVVVehKAq33HJLLkMbdnKticoHmV6jnlWhYFiiolgVLquaQVUYoeoM1I6zotiR5F42fXwxt187B4CwJCPLMoqi/gxL6kDv9mvnMH18cVbKBcMyFcX2aJzJegg1TnuStjGxTnfYx19L5/Nq1RLarUW8WrWEv5bOxx32atZ5vNmDy6G+nKTaY8rlsHC82ZN0H87039UYwbY2Jtz4ZaZ/5y4c48cz/Tt3MeHGLxNsS3Z1NTAwyB/Bjk6CLa24z5k5+MnDiF7tXyYrWEsX1OB2qmYiqfo5t9PG0gU1/crNnlxKRFYIR9QskATJoJodEpGJaGSHDDWrpNhl55L5pz+QETXG8nqzZ0ZrfxVobqb2X+9DDoeZ9YMHcIwfeYPVGK9sPcp/Pr+Lc6eWsfr2i3AWGBvKn2nkJBfzzTffBODmm2/m5ptv1jznyiuv5Morr8xFOHkjtvq1ZWcDWz9sxOuP4LCbuWLRBJYuGDfiHlZ6yPU1Ou2qNXd1eSG9vhC9/hCSDKIIRQ4rLqcVFAVBo/dZvngiU2qKeHbT/2/vzuOjKs+Gj//O7JPJJGQPJCHsRgKyKiKbuFCw4IJKRQsurdqnlSr1UStu1Va0lqp1e/vYxbdvsSrizqOCClo0ihBZRNksEMKWheyTzHrO+8dkYpKZBJgkMxPm+n4++Ugmc8+5c3Kca65z3/d172D73mN4VX8Vs1GD0/jxrNODkquutGvpZ7qNukY3DU0e1OYpNkkJJpKaN2k1hYqSzcfcnXsf+9/bAS3H1MH4SUzp4JjrtxwiLclCss3EsTonTpeP5uUiWMx60pIsGPQ61m85xJXnD2vTNh6uVYCCu/67zfeKTkf2zBlkz5wRpR4JIUJpLCkBwDZ4YFT7cTJr/1q/r04fl8e6TaW43L6QiWRnI1iBthaTHpfbFxTnzCY9ep0S1Hb25EFs3u2/sRUqxwoUlWk/O6TDdq2K0Wjt2l08ZRBrN53Y9g0pScFVMlvPngmVEHY2e6a3xaumw4fZft+DqG4XI377ILb8/tHuUkiqqvHCqm9485P/MKEwmzsXjA9Z10D0fqfWwrxeIDD61f7D96kk3N+xsqap5c3c4fRisxiYMjqn0zfzKaNzWLOhBINeR6PLi8ej+hMeHzTqvFgtBrw+lRkT8kO2T02yMHJIOjUNrpZjjhySHjTdpqvtAv1MbN4LKdTzGpo8HfYznGM6nF7sVgMGg4GcjMSQz9E0jYam0FXW4uFaFUL0Do2lBwFIyM09zjN7VuB9tTN6vRL0vhoYwXp6xWYamjxYjHr0egWfT8Pp8SeCHa6Nb9XWaNTRL8HWpq1ep4RsW3K0jiSbiTqHu6V6aEBglDApwUTJ0TrOKsxu086eYKS+0dNhwbTArJJAu8G5fVraHE//rKSgxwKzZ8prnM3LLb7vrX8arNLh7JnAOeoN8cqxfz/fPPBbNFWl8KEHsA0cEO0uheTy+HjiX1/x2bbDzJ48kJ9eMjJoOrE4dUgyKGLCjn1VPL1iMx6fisWox271r69Ys6GEdZtKWTRvTMh1g9PH5fHO+r0crfevNWi9NK7J5eVgeQMpdnPIu63hHjOcdl25KxzuMQNrVAyGjt/AY73KmhAiesK5QddTmg4dwmC3Y0xOjuhx22tZ+6fQMhtFbR6ls1u/n40S6n01UIU0MKvE1zyrZMSgzmejhNt2/ZZDpNrN2CxGKmuaWpYygH//wPQ+VszG4Nkh4c4qsZgN1Dd6aJ5RGkSngE6nBK3hD5zXrsyeCVckr/H6Xbv59qGH0ZlNjPjdgyTkRffGRkdqG1w8/MKX7Cyp4icXj+CSqYOiuk5X9LyobC0hRGtd2Yy3qs5JneP7zda1Vl8BdQ4XVe32Xwr3mIF2gXUTh485KDnawOFjjpZ1FKHadWVdQ7h97e1V1oQQ0bNjXxX3/08RazaUoKkadqv/w/qaDSXc/z9FIStm9qSm0oMx8eF5yugc6ho9HKpooNbhBg30OkCDWoebQxUN1DV6Qr6vhlOFtCttAxU6K2ub0ABTc4VOk0GHhn9qZqgKnYHKpxazf1bJ4NxkhuQmMzg3mZyMRCxmQ8jKp4qitLy2Xqe0OZ5Br0Ov14Vcwx84r06PD4NBR2qShf5Zdgb0TaJ/lp3UJAsGg67b41Ukr/Har7ez/f4HMSTaGPnI72LiWg7lSKWDO59ez3cHa7hrwZlcOm2wJIJxQJJBEXVd2Yx3+Xs70PAHnVD7BfoDk8Ly93Z0yzHXFZficHqpqGkK+UGgoqYJh9Mbsq/hVkwNt6+9ucqaECJ6unKDrqc0HjyENQaqLY4amkGdw4WmaSGrXmqaRp3DxaihGW3adeWchts23Aqd4VY+tVkMpDavBzQb9aCATwUUSLaZyMlIxGTQhRw1jXS8iuQ1XrWpmG8fehhzRjojlv4OS1ZWl1+zJ+wsqeK/n/o39Y0eHv7ZJCaNit1tLkT3kmRQdKiypolXP9rNL/+4jp88/AG//OM6Xv1od7d/AOjKZrwtm+O2zgLb/TewOW53HHNd8cGWkciOgmudw8W64oOdvjaEnkYTSrh97a1V1oQQ0dWVG3Q9wed04q2rw5Kdffwn97CteypIspn8e9GGqAytKApJNhNb97StSNyVcxpu23ArdIY7q2TK6BxQFBIsBpxuLznpNgb0tdM/y05KkhmDXulwdC/S8SpS13jFv9ezc+nvseblMXLpbzGnhb7pG22ffHWQe577DJvFyLJfTunw5rQ4NUkyKEKK5PSJrmzGGyiX3VkJ7EAZ7e44ZlWdE+04wVXTCJqWCuGf066cn3BHI4UQ8asrN+h6gqfOv/2NMTm48Eikrd9yiKQEEznpNv9ea4H9/xRIsjU/nmAKOjddOafhtg13f9twR+kC7ewJRlQNqupdJ9QuIJLxqqevcU3TOPjaG+z+45PYTy9gxG8fwJgU/eu3Pa9P5a9vbWfZi8UM7Z/CH345hX4dFJwTpy6pHCGCtJ8+EWAwKCQa/AHi6RWbeejmc7rlLl1XCp3odQoer+ovHBOiBDYaeH0aRkPb+x7hHtPt8aFTaNnfSW1ORhX8C+P1OgWdQtCaiK6c064WguktVdaEELEh3IqZPcVbVw+AwR79D9OBc6MoSoeVoUNVae7KOQ23bbgVOsOtfNq6XYLZQG2DG7vViEGvO27F1NavEYl41ZPXuObzsfcvf+Poe6tJnzKJobcuQmeMvb35aupd/P6fG9n+n2PMmTKIG+YUYtDLGFE8kr+6CBLpKUJdKXSSntwcVDrKk5R2z+viMU1GHarW+Sa+qqZhNrb9X6sr51QKwQghIincNWM9pWVkMMkekeN1pivr6cI9p4G2Xp9GdZ2LA2X17D9Sz4GyeqrrXHh9WofHNBr0nY5iGg36Tiuf9k2zcajSwb4j9RyqdNA3zcZt88d2OEoXGN2bM3UQOp1CWXUTKMTcbJSeusZ9Tic7Hvk9R99bTc7cSxn2q9tiMhHcfaCaxU98zO4DNfzq6rHcdOlISQTjmPzlRZBITxEKTC1pdHpDBrpGp7fjqSWK/828o72QWh5X2j4h3GkwSTYTPtWf/ClK8Jc/KcRfXryVrpzTLp0fIYQ4SbF2A8rTPDIYC9PsurKeLtxzGm4F065U6OxK5dP0PlZ+PPN0fjlvNB6vysyzB3Dl+cNian16T1zj7poatt9zP9XFmxn0sxsZcO0CFF3sfcxe/UUJdz3zKTq9jj8smiKfHYQkgyJYV9aohSO9j5U5UwZTXt1IdYMLTdXQKRqaqlHd4KK8upE5UwaHDCQ+FdKT/dXLNC34C/w/V9vtUxvuYvXsVBu65imoWut9LJqPp+DfSyk71damXVfOaVfOjxBCnKxYq0Tsa2yuiGlLiMjxOtPV9XTh3NQLt4JpuH3trkqb543PY+xpmbzwv99y9Jij0+dGWndf440HStl25900lh7k9CV30XfWzO7sbrdwe3w88+oWnnl1CyMHp/HEbdMYlBPdfTtFbJBkUATp6vSJk61CWlnTxDvr/0Oq3YzZqMPr03B7/VNizEYdqXYz76z/T8j2NouBRKuJjD4W2td0URTI6GMh0WrqcBrMdbMLUVDYf7Se/xyqY//RehQUrptdGHI6S2WtE51OQddcObT9voaBn7XfVDfcaT6tz09GHyt9Ek0oOn8FOEWn0CfRREYfa4fnRwghTlbMVSKOoW3Owj03XbmpF24F03D72l1LRRRF4ZYrR6PXKTz58uagQm7R1J3XeNWXG9l2592oLjcjHn6I1DPH92jfw1FaVs/tf/o3q78o4crzh/LAjRP9U4eFQJJBEUJXpk+EUzHTv3efh+oGNx6vitHg36TWaNDh8apUN7hxOD0drqc7VuekosYZNFVU06CixsmxOmfIvr5XtJ9H/vElZdWNGPUKJoOCUa9QVt3II//4kveK9ge1cXl8mI36DoOaT9UwG/VBBWS6slFxIDDbrMaQU31sVmNEy7wLIU59sVWJuDkb7Gg9QISFs56uKzc9AxVMs1ITWuKi26u2xMus1ISQFUzD7WtgWYPXq1JV52y+eVnHgbJ6quqceL3qCS8VyUixcvNlZ/DN3mO88sGuEzzDkdHVa1zTNEpffY0dS3+PpV8/Ri37PfahQyLU+xOjaRqrvyjhtic+obreyQM/PZuFFw1v2QpLCJBqoiKE6ePyWLepFJfbF/LO4IlOLQk4XsXMdcWl1Ds8/j0B282vD5TFrnf4k8H2Fcbys5Oob/TfxQu124OmQX2jh/zstmtNdh+o5vk3twFgbLVoWlH8d0h8qsrzb25jcG4yw/qnfP9z/FM+O+NweklMaHvHbdTQDP61eqf/d9K1PZ5e8R8v1DQfOLn1hlIxVAjRXWKlErESmIkRIyNLO/ZV8fSKzXh8KjnptpZKm4H1dIvmjQlKJAI3PesbPWha2wrXgZue9gQtZJxzOL0YdAoVNU1oGhgNOv8adQ08Xo2yqkYy+lhD3qQMp68OpxejDsprnGiahk5R/JVINahzuKlv9JDZx4JXDTpcSOeNz2Prngpe/mAXhYPSQsa5aAn3Gvc5nex56lmOfVZE+tQpDLnlv9CbzT3Uy/DUNrh47rWtFG07wuihGSy+emzI6rdCyMigCBLpqSVVdU40tM737kOjOsTefas+3Ru8hg+C1vCt+nRvm3bL39uBqgUnnwF6nQ5V8z+vNUfz73887Z8X7jQfiPwaTiGEiClK8/t0DIwMhrueruWmJ/74oihKy1cgDgVuerZnNuopr25eNxlizSBAeXUjpnY3DcPtq9moo7za2RyjwONrHon0qc3r8TXKq52YjCf+EfJnc88gJyORPyzfRFlV4wm3i0XO8nK+/vW9HCv6nPxrFzDsV7fGXCK48duj3LJsHV9+c5RrfzicB2+aKImg6JAkgyKkrkwt6UyoqSVuj3rcJSEK/j2T2tu+9xh6nYLRqPMHxea7pf5WvJY9AAAgAElEQVRRxu8f3773WHC74xxUrxDUruEEk8H2z+vKNJ9YK/MuhBCRpDP444rqObH3354UjZue6ckWVI1O26qa/3nd0df0ZCs+VcXn8++ni/b9vr0+VWt+XA3asqkzVrOBe2+YgNen8bu/b6DJ1TtvXtZ+vZ1t/30XzvIyht+3hNy5lzYn57HB0eThmVe38NDfNtAn0czjt03jivOGttQ5ECIUSQZFSOGUlQ53BMtk1HO82T+qRtBdT/AHJkUBBQWDXofJoMds1GMy6DHodSgoKApB02cC7ToTql24HE4vbq9KWVUjHq9/ipDZGFgX6Z/m4/aqIUf3Yq3MuxBCRJIhMREAb0NDlHsSnZuelbVNLUsmQlE1Db2iUFnbdoQv3L6WVTXSnAPSPAjZ8hXYQknVOOkRvpyMRO5aMJ4DZfU89s9NeH0nOM80BmiqSumKlWy//0EMdjtnPPYoKePGRrtbbXz57VF+8Ye1fLChhMunD+Hx26YysJ9UCxXHJ8mgCBLu1JJwR7BSk/yVQDsLdIpCyCkO/rLa/mkrXp+K2+PD5fHh9vjw+tSWaS7tF0u3tKO5nbe5nbe5HaHbhSvcaT4Qe2XehRAikgzN+wt6amuj3JPo3PR0eVQyU/zxL9QyA4DMFAvudolkuH2tdbhPaPlFrcPd+S8UwpjTMvmvuWewaUcZf3plM2qMrAPtjKeujm8fepgDL75E+uRJjFr2exJyY+fma029iz8s38Rv/7aBRKuRP/xyKtfNLsRo6PxGgBABkgyKIOFOLQl3BGv6uFySbP759j7VH+AgEOj8gSLJZmb6uNyg1xsxKA2vquHxqv62zY9rza/l8ap4VY0Rg9KC2vlUDY9H/X4ajELLNJjA4+3bnWhu2P554U7zgRgs8y6EEBFkTvO/D7srjx3nmT0vGjc9bRYDRoOenHSbfzuAwOsokGQzkZNuw2jQBx0z3L66Pb4TWn7Rvmr2iZo5cQA/nlXAx8UHef7Nr5tjfmyq27GTLbfdTu32bxj8Xzf71wdaYyPW+lSN94r28bPff0TRtsNc/YMCnlh8bpuid0KcCEkGRZBwp5Z0ZTNem8VARh8ryc2BzqcCCiTb/Pvo2SyGkCNfsycPah7h8ydz7b80/EFs9uRBQe2CpsFA0DSY9u1Skk5skXj754U7zScgtsq8CyFE5JhSU1D0elwVwQW2Ii0aNz0DxzQYdCG3FzIYdCGPGW5fTUadPzZ2svxCw19oJlzzzh/GZecO4X8/28ezK7fG3AihpmkceuMtvl5yHzqjiTMeW0r2zBkxsz5wT2k1dzz1b557bRuDc5J56vbpzJ9xWpsqtUKcKKk4IYI4nF7s1s4vDb3en5C0FhjBenrFZhqaPFiM+pYy1k6PPxHsaDPeQDujUUe/BFubdnqd0uHIV8nROpJsJuoc7pbpKwGB0JKUYKLkaB1nFWa3aWdPMDaX+Q79O9oTjEHt+mclcaz2+B9I+me13coiMM2nvMaJT1XbjBD67wgrIaf5tBYrZd6FECKSFL0ec1YmTYeOv69dTwt366VAO4tJj8vto77JjU8FnQ6SE0yYTXr0OiXkTc+uHvNk26UmWTha2YiqhS54o2oaCgopXahOqSgK188ejkGv8OpHe3B7fCyaNyYmkhl3TS3fPfMs1RuLSZt4NkMW/RyDzRbtbgFwrLaJ//fuDtYVl9In0czt14xj2picmElSRe8U/f/rRMzpSvXKcEewwm23fsshUu1mcjMTsZr9/Qn03Go2kJuZSGqSOWgUc/2WQ6QlWcjNsGE161sPDGI168nNsJGWZAlqV1nrxKBXOpwuqlPAoFeorG1bES7caT5CCCEgoX9/HCXB2y5EWrjT9gPtAtMs+6XZyM9OpF+arWXaZUc3Pbt6zJNtN31cHnabvz5AR2sU7TZjl9epK4rCglmn8+OZBawrPshDf/3ihLdv6ilVGzex5ZeLqdmyjYE3/oTT7vrvmEgEnS4vL3+wi5sf/Yh/bz7E3HOH8H/uOp9zx+ZKIii6LO4/eVbWNLGuuJT1Ww7hcHqxWQxMGZ3D9HF5cbsGa8roHNZsKMGiKNQ73NQ3uVGb72DarSbsNhNOj48ZE/KP+1rhTvw40XYto5iKQoLZgE9VW/qaYDZg0OvQ6wgaxQy0MxgM5GQkhu6DpgW1c3l8ZKYkUFHThE7zJ3+BzX/V5nWHGX2sQWspAuc00WokNckScl1IQ5PnhM7pyZJrXAjR29kG5FP15UZ8TU1RX7MVuHkZeF9taPKSYDEwY0J+p++r4baL9DEDI4pmox6Xx0dDkwdV1dDpFJISTJiN/umi3VG0TFEUfnThaaQlW3nm1S3c+cx6llx3Vodxuaf4nE72/f0flK1eg23gAEb87jck9O8f0T6E4vGqrP5iP698uJuaeheTzujHdbOHk50W/QRVnDriOhncsa+Kp1dsxuNTsRj12K3+EbE1G0pYt6mURfPGxOVarOnj8nj/8/0cLK8H/KNgep0/4al1uKl1uEhNsoQMBOGe03Db2SwGHE1ejtU50ZqTs9Z9rWt0k5Zk6XBhvcHQ8R21UKOfNosBTdXIyUhsSZRbT/Wx20ygaSjthg7Dna7TVXKNCxG7GhoauOqqq/jzn/9Mbm4uRUVFPPLII7hcLmbNmsXixYsB2LFjB/fccw8Oh4Px48fz4IMPYjDEV/i2nzYMVJX6Pd/R54yR0e5O2NP2uzLdP1LHbL10QwP6JZjaLN0wdLDkoysuOKs/GX2s/P6fm1j8xCfcdtUYzjmjX7e9fmfq93zH7sefxHnkKDmXXUL/a+ajMxojcuyOeH0qHxeX8tIHuymvaqRwUBpLrj1L4rXoEXE7TTTc7RPij3+M7vt1dR2P2YV7Trvytxh7WiYVzY93tGVDRU0T407LbNMu3IX1LQv59QopSebmhfx2+mfZSUkyY9ArIdtFoyKoXONCxK6tW7cyf/589u/fD4DT6WTJkiU899xzvPvuu2zfvp1PPvkEgDvuuIP777+f1atXo2kaK1asiGLPo8M+bBgoCnXf7oh2V+JCNIqWjRqWwZO/mkZeViKP/GMjz7y6pUc3p9d8PkpXrOTru5agutwUPvQAA65bGNVE0OP18V7RPm5+5EP+9MoW7AlGHrxxIo/8fJIkgqLHxG0yGO72CfFgXXEpOp1CbkZiyPVtuRmJ6HRK0LkJ95xG4m/RPoXtSuXTcPf8i3RwlWtciNi1YsUKHnjgATIz/Teqtm3bRn5+Pnl5eRgMBubMmcP777/PoUOHcDqdjB49GoC5c+fy/vvvR7PrUWFItGEbNIiaLVuj3ZW4ERhRfOr26fz1ngt56vbpXHn+sB5dXpCZksCjv5jM3HOHsGZDCYuWrWPbd91fRdaxbz9b77ibAy++RNo5ZzPmqcejOuJc3+jm1Y9289OHP+S517aRYrdw/08m8MRt0xhbkCnrAkWPiq95Jq2czPYJ8Va9MXBuAmWsQ61vszQ/r/W5CfecduVv8dWucjL6WDlW13GVzow+Vr7aVc51swtbftYdlU9Ppl3r9pGqCCrXuBCx6+GHH27zfXl5ORkZGS3fZ2ZmUlZWFvR4RkYGZWVlEetnLEkZN4aDK1/HU1+P0W6PdndEDzEa9Fw/p5CzCrN58uWvuOf/FDF1TA43zCkkLblriajq8VC6YiWHXnsDQ2Iip915O+mTzummnp+80rJ6Vn26l4+al5GMHprBr+aP5Yyh6ZIAioiJ22Qw3O0T4kG45ybS7Vq3tZhs1DW6gxa6JzWvdQjVNhpFACJJrnEheg9VVdt8+NOab2Z19Hg8Sj3rTA6uWEnVho1kXXBetLsjeljhoDSeueM8Vn60h9fW7eHLb45y8dTBXHbukJalDiejftdu9jz9LE2lB8mYfi4Db7gOY1Lkbyq4PT4+23aY1V+U8M3eY83FeHK5eOpgBvRNOv4LCNHN4jYZDLeASDzoSnGVrrRDocPqpWih/xbfH7PjUUyvVz2hv+PJVD7tDXv+yTUuRO+RnZ1NRatN1SsqKsjMzAx6vLKysmVqabxJHDIYS3YWles/lWQwAmKhErXZqOeamQWcNz6Pf763gxUf7uZ/P9vHxVMGcdE5A+ljNx/3NXxOJwdefInD7/wvprQ0ht9/Dynjxkag999TVY1v9h3j4+KDfLbVfz77ptu4fvZwzj+zP8mJx/89hOgpcfspsKXUfycbnJ7o9gmnmnDPTVfarfp0H/WN7g4rgtoTTMyePLDb+gqnfqVNucaF6D1GjRrFvn37KCkpITc3l1WrVnH55ZeTk5OD2WymuLiYcePG8dZbbzF16tRodzcqFEUhfeoUDq58HVdFJeaM9Gh3qVcIJ6mLtfjYN93GnQvGc8V5Q/nX6p28tGYXK9fu4dyxucw4O5/T+qeEHDGv+nIje//yd1zl5WTP+gH5C3+MISEhIn32+VS+2XeML7Yf5fOvj1BZ04TFpGfiyL6cP74/I4eko+to02IhIihuk8FolfrvDcI9N4F2jU4vLrcvaITPbNKHbDdqaAb/Wr0TAL3u+8RFUUCv+De9rXO4GDU0g/bC7Wv7SpsBBoNCosFfIObpFZt56OZzYmba58mSa1yI3sNsNvPoo4+yaNEiXC4X06ZNY+bMmQAsW7aMe++9l4aGBgoLC1m4cGGUexs9WRecz8FXX6Psgw/pf/VV0e5OzAsnqYvl+DgoJ5l7b5jAwfJ63vr3XtZuKuWDLw+Qm5nIuWNzOXtEX/pn23GVlbH3r3+nemMx1rxcRix9iOTCwuMfoIsqa5rYsruczbsq2Ly7nPpGDyaDjjGnZXLdD4czoTAbizluP3qLGBW3V2RXC4GcyrpSXGXOlME8/+Y2VM2fyOkU0FSobnChU+CmS88Iard1TwVJNhP1jZ4Oi8DYE4xs3VPBsP4p3dLXQKXNjtYdmE16Gpo8rCsujempoJ2Ra1yI2Ld27dqWf0+cOJG333476DkFBQWsXLkykt2KWZasTFLGjeXo+6vJufwy9ObeNb0uklMvw03qekN8zM2084srRnH97OF8uvUwH355gOXv7+Tld7dzgWs3ZxzZjGIwkD3/agZdcQm6HtiX0+P1ceBoPbsPVLOzpJod+6s4UukAIMVuZvzpWZw9oi9jT8uUBFDEtLi+OntLIZBoOH1gKrfNH8vy93awfe8xfKqGXqcwYlAaP551elBSBv7A8876/5BqN1Pf5MHp8qEBCmAx67Fbjbyz/j+cOTyrzbldv+UQSQkmEixGjtU2BbVLS7Zi0CkdVr0Mp6+BSpterxpUeCbRaiQpwXRKVNqUa1wIcarJmXsJ25fcT/mHa+n7w1nR7s4Ji/TUy3CTut5UiTrBYmTGhHxmTMin5JMiDvz9/6KrOcaupIF8kDqOho0G0nZ/xMB+yQzom0R2mo2sVCsZKQkk2UzYLMYOp2r6VI2GRje1DS6q612UVTVSVtXIkUoHJUfrOFjegKr6qw30STRTMCCFi84ZwOhhmeRn2+O20JPofeI6GYTeUQgkGloHrZx0W8uI0pFjDp586auQQWtdcSkOp4f6Rg+apmFstVbN41WpbnBjT9CCAo/D6cWgU6ioaULTwGjQoSj+NYMer0ZZVSMZfaz41NAlXsLpq8PpxaiD8honmqahUxT/iKQGdQ439Y0eMvtY8KrdeFKjRK5xIcSpJGn4cOynF3DwtdfJvOC8XjE6GI2pl+Emdb2tEnXjgVL2/+OfVG8qxpaby6Dbf8PZhYVMPlTLt/uOsfdQLfsO17F5V3nQ5widAlazAb1eh0GvoCgKbo+Kx+vD5fGhtfvYodP5t6vqn21nQmE2A/omMax/ClmpCZL8iV4r7pNBEawrU0vqHR5Q2q79A9ArCqqmUe8IvgtpNuo5UtmAoijoW92hC6wZVDWN8upG+qYndltfzUYdRysb0dD8Saeqomn+Y/qnqWqUVzvJTo/MQnMhhBAnRlEU8hdczfYl93Pknf8l94q50e7ScUVj6mW4SV1vqUTtrq7mwEsrKPvgQ/RWCwOuW0jf2RehM/rP8bD+KW1mBvl8KpW1TsqqHFTWNFHf6KHe4abR5cXrU/H5NDRNw2TUYzToMJv0JNvMJCeaSE40k5WaQEYfK3p9x0XZhOiNJBkUQcINWlV1TjQ09EroN0qdouDTVKrrnG0eT0+2cKiiAWMHUzV0ioJH1UhPDt42Ity+pidbOVTegKbRMiVVAdDAp2n+7xWN9C5ucCuEEKL7JRcWknrWmZS++hoZ507DnJ4W7S51KhpTL8NN6mK9ErXP6eTwW+9w8PU30Twe+l40k7wfXYkxqfM9+vR6HVmpCWSlyk1eIVqT2xsiyMkErdbcHpXjTZJQAJen7dzLytqmlpHDUFRNQ68oVNY2dVtfy6oaUQOJYCATbP5qni2KqvmfJ4QQIvYM/On1oKrs/cvf0DqIH7HC4fSi13ceIfV6hUZn9029nDI6B6fH1+lznB4fU0bntHls+rg8jHr/zJpQolWJWvP5KPtwLV/91yIO/OtlUsaMYswzTzLoxp8cNxEUQnRMkkERJNygZTLq6WBZXwtV8z+vNZdHJTPFP+rnU1U0TWv58qn+xDEzxYLbE7yAL9y+1jrc6BR//qdpfL/jvOb/XsG/lqDW4e78FxJCCBEVlqws8q6aR9UXG6j4ZH20u9OpwChdZ7p76mW4SV2gEjVAQ5MHr9cfl71elYYmD0BEK1Frqkrlp5+x+Ze/4runn8WUlsbIR35Hwa/vxNqvX0T6IMSpTJJBESTcoJWaZEFpXuMXin+bCP/z2h/PaNCTk24jyWaCwGsokGQzkZNuw2jQhwyS4fbV7fGh1ykYjTr/OsXmgjX+9Y7fP+4+zl1VIYQQ0ZNz6cXYC05j7/N/wVlWFu3udCjcUbqu6EpSF6hEPWNCPorOv65Q0SnMmJDPQzefE5EN5zVV5djnX7DlttvZ9YfHQYHT7rydM/7wCEnDT+/x4wsRL2TNoAgS7nqB6eNyWfXpPuob3fhUzT/y1pxkqc3FWZJsZqaPyw19PKuR1CRLULII/kAWan1CuH01Gf13S3UoGDoYWVTRsBjlfokQQsQqRa9n2K9uZcviO9j56B8Y+ejDMVlddPq4PNZtKsXl9mE2BS9t6Kmpl13ZXihalag1TaN64yYO/OsVHPv2YenXj2G330b6pHNQ9J0vCxFCnDxJBkWQcINWoJ3FpMfl9lHf5Mangk4HyQkmzCY9ep3SYbtwgmS4bVOTLBytbERt3laiPVXTUFBICZGYCiGEiB2WrCyG/epWdvx2Kd898xzDFt+KooutG3mBUbqnV2ymocmDxahv2QbJ6fHHqZ6aetlbthfSVJWqjZs4uGIlDd/9B0t2NkNvW0TG1CmSBArRg2Lr3VLEhHCnlgTaBaZZ9kuzkZ+dSL80W8u0y87anezxutJ2+rg87DZ/BdKO1inabcaIL5AXQghx8lLHjyN/wTVU/vtTSv75YrS7E1IsTL2MRarHQ9kHH7L5llvZufT3eOrrGbLoF4x97ikyp58riaAQPUxGBkVI4U4tiXS7cNsGRhTNRj0uj4+GJg+qqqHTKSQlmDAb9RiiUC1NCCFEeHIuvwxneQWHXn8TfUICeVdeHu0uBekto3SR4HU4OLr6Aw6/vQpPdTW2QQMZdvti0idNlARQiAiSZFB0KNygFel24bRtPWVHA/olmNpM2TH04JQdIYQQ3U9RFAbf/FNUl5MDy/8FmkbulZejhFgKIKLHVVHJkXff4+j7a/A1NpI86gxyb1tE8qgz5G8lRBRIMijiVldGI4UQQsQeRa9n6C9vAeDAiy/hqatn4A3XxtwawnijaRq1W7dx5N33qdq4CYD0cyaSc9klJA4ZHOXeCRHfejQZbGho4KqrruLPf/4zubm5FBUV8cgjj+ByuZg1axaLFy8GYMeOHdxzzz04HA7Gjx/Pgw8+iMEgearoeTJlRwghTi2KXs/QWxdhsNs58s4qXOVlDL3tVgwJcoMv0rwNDsrXrePoe6tpOnQYQ1ISOZddQvYPZmDJyox294QQ9GABma1btzJ//nz2798PgNPpZMmSJTz33HO8++67bN++nU8++QSAO+64g/vvv5/Vq1ejaRorVqzoqW4JIYQQ4hSn6HQM/Mn1DLzxJ1RtLObru+6m8UBptLsVFzRNo37Pd3z33J/ZeMON7PvrC+htNobetogz//Y/DFj4Y0kEhYghPTb8tmLFCh544AHuvPNOALZt20Z+fj55ef6CHHPmzOH9999nyJAhOJ1ORo8eDcDcuXN56qmnuPrqq3uqa0IIIYQ4xSmKQr/ZF5GQm8Pux59k6+13MvDGG8i68AJZm9YDXMeqqPjk35SvXUdT6UF0JhPpUyfTd9ZMmQoqRAzrsWTw4YcfbvN9eXk5GRkZLd9nZmZSVlYW9HhGRgZlZWU91S0hhBBCxJE+o0cx+snH2f3En/jPs3/m2GefM/gXP8OSKaNTXeVzuajasJHyteuo2boNVBV7wWkM/vnNpE+ahCHRFu0uCiGOI2IL81RVbXMnTtM0FEXp8HEhIqGypqmlgIzD6cVmMTBldI4UkBFCiFOIKTWFwgfv5+j7a9j/j3+y+ZbbyL1iLjmXXozOZIp293oV1e2mZstWjn3+Bcc2fInP0YgpPZ3cyy8jc/q5WHP6RbuLQoiTELFkMDs7m4qKipbvKyoqyMzMDHq8srKSTLlbJyJgx74qnl6xGY9PxWLUY7ca8Pk01mwoYd2mUhbNGxO3mwALIcSpRtHp6HvRTFLGj2X/3/8vB158ibIPPqT//B+RMW2q7G3XCZ/LRc1Xm6ks+pzqjcX4mprQ22ykTTiLjOnnkjyiUCq2CtFLRSwZHDVqFPv27aOkpITc3FxWrVrF5ZdfTk5ODmazmeLiYsaNG8dbb73F1KlTI9UtcYo42RG+ypomnl6xGYBEq7HlcYNBIdGgw+X28fSKzTx08zkyQiiEEKcQS2YmBb++k5otW9n//5az50/PcPC1N8i9/DLSp0xGZzQe/0XigLu6mprNW6jaWEx18VeoLhcGu530yZNIO+dskkeOkHMlxCkgYsmg2Wzm0UcfZdGiRbhcLqZNm8bMmTMBWLZsGffeey8NDQ0UFhaycOHCSHVLnALCGeFbV1yKx6e2SQRbM5v0NDR5WFdcKttOCCHEKajP6FGMGnUGx4q+oHTFq+z50zOULH+J7Fk/IOvC8zH16RPtLkaU6vFQv3MX1Zu3UPPVFhz79gFgTOlD5nnnknbORJILh8sIqhCnmB5PBteuXdvy74kTJ/L2228HPaegoICVK1f2dFdEhERyHV64I3zrtxzCYuw8oFmMetZvOSTJoBBCnKIURSF90kTSzjmbmq82c+jNtzmw/F+UvryClLFjSJ88iZQzx5+SexSqXi+Ovfuo27GTuu3fULPta1SnE0Wvx356AfkLrqHP2DHYBuTLFFAhTmGys7voVpFehxfuCJ/D6cVu7fzy1+sVGpq83dZXIYQQsUlRFFLGjSVl3FgaDx6ibM0HVH76GVVfbkRnMpEyzp8YJo86A6PdHu3uhsXb2Ej9zl3U7dhJ/Y6d1O/ajep2A2DJziLz3Kn0GTuG5JEjMCQkRLm3QohIkWRQdJtorMMLd4TPZvEnqQZDx5VrfT6NBIv8LyKEEPEkITeHgTdcx4DrFlK/cxeVnxZRWVTEsc83gKJgGzCApBGFJI8sJLlwOIbExGh3uQ1N03BXVtKwdz+N+/fj2Of/ch496n+CTodt4ECyZlxA0vDTsRcUYE6TYmlCxCv5pCu6TTTW4YU7wjdldA5rNpSQaOh46ovT42PGhPxu6acQQojeRdHpSBp+OknDT2fgT66jfvcear/eTu3X2ylbvYYj76wCRcGSlUVCfh4J/fuT0N//X2tOvx4trqJpGp6aGpxHy3AePYqzrLzl340HSvE5HC3PtfTNxjZwAJnnnYv9tGEkDht2Sk57FUKER5JB0W2isQ4v3BG+6ePyWLepFJfbh9kU3GeX24dRr2P6uLxu6acQQojeS9HrSTq9gKTTC8ibd4W/2MruPdR98y2O/ftpLCmlamMxqGpLG4Pdjik1BVNqqv8rpQ96mw292YTOZEJnMvv/azaBpqF6vWgej/+/Xi+qx4OvsQlPXR3eujo8tXV46urx1NXiqalFdbladVDBlJaGJTuL9MmTsA3MxzZwILb8/uitkvgJITomyaDoNtFYhxfuCF96HyuL5o3h6RWbaWjyYDHq0esVfD4Np8efCC6aN0a2lRBCCBFEZzSSXDic5MLhLY+pHg9Nhw7ReKCUpsNH8FRX466uxl1VTeOBA7ira9okiyd8LJMJQ1ISxuQkjHY71n59MSYnYcnOwpyVhSU7G0tmBjqTqTt/RSFEnJBkUHSbaKzD68oI3+kDU3no5nNaKp82NHlJsBiYMSG/RyqfCiGEOHXpjEZsAwZgGzAg5M81VUV1uVDdblS3G5/L3fJvRVFQjAZ0BgOKwYDOaEQxGNBbLOgsFhSl47gqhBBdIcmg6DbRWIfX1RG+9D5Wrjx/mGwfIYQQokcpOh16q1WmbQohYopsHCO6zfRxeRj1/qqhofTUOrzACN+MCfkoOv80VEWnMGNCPg/dfE63bmUhhBBCCCHEqUJGBkW3ieY6PBnhE0IIIYQQ4uRIMii6lazDE0IIIYQQoneQZFB0OxmlE0IIIYQQIvbJmkEhhBBCCCGEiEOSDAohhBBCCCFEHJJkUAghhBBCCCHikCSDQgghhBBCCBGHJBkUQgghhBBCiDgkyaAQQgghhBBCxCFJBoUQQgghhBAiDkkyKIQQQgghhBBxSDadF0IIIWLEq6++yvLly1u+P3jwIJdccglNTU0UFxdjtVoBuOWWW7jwwguj1U0hhBCnCEkGhRBCiBhx5ZVXcuWVVwKwZ88efvGLX3DLLbdw7bXXsnz5cjIzM6PcQyGEEKcSmSYqhBBCxKDf/OY3LF68GKU5h1gAAAqqSURBVKvVyuHDh1myZAlz5szhqaeeQlXVaHdPCCHEKUCSQSGEECLGFBUV4XQ6mTVrFpWVlZx99tksXbqUFStWsGnTJlauXBntLgohhDgF9Pppoj6fD4CjR49GuSdCCCF6WuC9PvDef6p6+eWXuf766wHIy8vj2WefbfnZggULePPNN5k3b95xX0dipBBCxIdw42OvTwYrKioAuOaaa6LcEyGEEJFSUVFBfn5+tLvRI9xuNxs3buTRRx8FYNeuXezfv58f/OAHAGiahsFwYuFbYqQQQsSXk42PvT4ZHDFiBC+++CIZGRno9fpod0cIIUQP8vl8VFRUMGLEiGh3pcfs2rWLAQMGkJCQAPiTv6VLl3L22WeTkJDAK6+8wmWXXXZCryUxUggh4kO48bHXJ4MWi4Xx48dHuxtCCCEi5FQdEQwoLS0lOzu75fuCggJuuukm5s+fj9frZcaMGcyePfuEXktipBBCxI9w4qOiaZrWA30RQgghhBBCCBHDpJqoEEIIIYQQQsQhSQaFEEIIIYQQIg5JMiiEEEIIIYQQcUiSQSGEEEIIIYSIQ5IMCiGEEEIIIUQckmRQCCGEEEIIIeKQJINCCCGEEEIIEYd6/abz4VqwYAFVVVUYDP5T8NBDD7Fs2bKgx0aNGhXNbkbF2rVreeaZZ2hqamLSpEnce++9FBUV8cgjj+ByuZg1axaLFy+OdjejItS5ufvuuykuLsZqtQJwyy23cOGFF0a5p5H36quvsnz58pbvDx48yCWXXMIFF1wQ99dOR+emqalJrh3grbfe4vnnnwdg6tSp3HXXXezYsYN77rkHh8PB+PHjefDBB1vem0XPkxgZmsTHzkmMDE3iY8ckPnYuIvFRi0OqqmqTJ0/WPB5Pp4/FowMHDmiTJ0/Wjhw5orndbm3+/Pnaxx9/rE2bNk07cOCA5vF4tBtuuEH7+OOPo93ViOvo3MyePVsrKyuLdvdiyu7du7ULL7xQO3z4sFw77QTOzbFjx+Ta0TStsbFRO/PMM7Vjx45pHo9Hu+KKK7TPPvtM++EPf6ht3rxZ0zRNu/vuu7UXX3wxyj2NHxIjQ5P42DmJkSdG4mPHJD62Fan4GJfTRPfu3QvADTfcwMUXX8zy5ctDPhaPPvjgAy666CKys7MxGo088cQTWK1W8vPzycvLw2AwMGfOHN5///1odzXiQp2bgoICDh8+zJIlS5gzZw5PPfUUqqpGu6tR95vf/IbFixdTWloq1047gXNjtVrl2gF8Ph+qqtLU1ITX68Xr9WIwGHA6nYwePRqAuXPnxv11E0kSI0OT+Ng5iZEnRuJjxyQ+thWp+BiXc27q6uqYOHEi9913Hx6Ph4ULF1JbWxv02MCBA5k0aVK0uxtRJSUlGI1Gfvazn3HkyBHOPfdchg4dSkZGRstzMjMzKSsri2IvoyPUubn88ss5++yzeeCBB7Db7dx8882sXLmSefPmRbu7UVNUVITT6WTWrFmsWrVKrp1WWp+b0tJSuXaAxMREbr31VmbNmoXVauXMM8/EaDS2uW4yMjLi+rqJNImRoUl87JzEyOOT+NgxiY/BIhUf43JkcMyYMTz22GPY7XZSU1O54oorqK2tDXrsk08+iXZXI87n8/H555+zdOlSXnnlFbZt20ZpaSmKorQ8R9O0Nt/Hi1DnZtOmTTz77LNkZmZitVpZsGBBXF43rb388stcf/31AKiqKtdOK63PTV5enlw7wM6dO3nttddYt24d69evR6fT8dlnn8l1E0USI0OT+Ng5iZHHJ/GxYxIfg0UqPsZlMrhp0yY+//zzlu81TWPnzp1Bj8VjsYL09HQmTpxIamoqFouFCy64gKKiIioqKlqeU1FRQWZmZhR7GR2hzs0bb7zB6tWrW54Tr9dNgNvtZuPGjZx33nkAZGdny7XTrP252bVrl1w7wKeffsrEiRNJS0vDZDIxd+5cNmzY0Oa6qaysjNvrJhokRoYm8bFzEiM7J/GxYxIfQ4tUfIzLZLC+vp7HHnsMl8tFQ0MDb7zxBhMmTAh6LB6rFk2fPp1PP/2Uuro6fD4f69evZ+bMmezbt4+SkhJ8Ph+rVq1i6tSp0e5qxIU6NxdccAFLly6ltrYWj8fDK6+8EpfXTcCuXbsYMGAACQkJAIwaNUqunWbtz42maXLtAAUFBRQVFdHY2Iimaaxdu5azzjoLs9lMcXEx4K+mFq/XTTRIjAxN4mPnJEZ2TuJjxyQ+hhap+Bh/aTb+N6ytW7dy6aWXoqoqV199Nddeey0ej6fNY2PGjIl2VyNu1KhR/PSnP+Xqq6/G4/EwadIk5s+fz6BBg1i0aBEul4tp06Yxc+bMaHc14kKdmwULFmAwGJg/fz5er5cZM2Ywe/bsaHc1akpLS8nOzm753mw28+ijj8b9tQPB56agoICbbrop7q+dyZMn8+233zJ37lyMRiMjR47kpptu4sILL+Tee++loaGBwsJCFi5cGO2uxg2JkaFJfOycxMjOSXzsmMTH0CIVHxVN07Ru6rMQQgghhBBCiF4iLqeJCiGEEEIIIUS8k2RQCCGEEEIIIeKQJINCCCGEEEIIEYckGRRCCCGEEEKIOCTJoBBCCCGEEELEIUkGhYiiDRs2nHS55BtvvJHvvvuu0+f8+te/5m9/+1vInz3zzDN8+OGHJ3VMIYQQIpIkPgoRGXG5z6AQvdlf/vKXLrXfsGEDQ4YM6abeCCGEELFB4qMQJ0+SQSGirLGxkcWLF7N3715cLhe/+93vOOOMM1i2bBkbN27E5/MxfPhw7r33XhITEznvvPP405/+xMiRI3n++edZuXIlNpuN8ePH89FHH7F27VoANm/ezFVXXUVlZSVDhw7lj3/8I2+88Qbbt2/nscceQ6/Xc+GFF0b5txdCCCFCk/goRM+TaaJCRNnRo0e57rrreOutt7jqqqt4+umnef7559Hr9bz++uu8/fbbZGZmsmzZsjbt1q9fz+uvv87KlSt5/fXXcTgcbX5eVlbGCy+8wOrVqykrK2PNmjVcc801jBgxgjvvvFMCnRBCiJgm8VGInicjg0JEWV5eHqNGjQKgoKCA1157jY8//pj6+nqKiooA8Hg8pKWltWn3ySefMHPmTJKSkgC45ppr+OKLL1p+fsEFF2C1WgEYOnQoVVVVkfh1hBBCiG4h8VGInifJoBBRZjQaW/6tKAqapqGqKkuWLGHatGkAOBwOXC5Xm3YGgwFN01q+1+v1QT9v/7pCCCFEbyHxUYieJ9NEhYhBkydP5sUXX8TtdqOqKvfddx+PP/54m+dMmzaNNWvWUF9fD8DKlStP6LX1ej1er7fb+yyEEEL0NImPQnQvSQaFiEE///nPycnJ4bLLLuOiiy5C0zR+/etft3nOxIkTmTdvHj/60Y+YO3cu9fX1LdNeOnPeeefx+OOP88Ybb/RU94UQQogeIfFRiO6laDI2LkSv9PXXX7N582YWLlwIwAsvvMDWrVt58skno9wzIYQQInokPgpx4iQZFKKXamhoYMmSJezduxdFUejbty+//e1vycrKinbXhBBCiKiR+CjEiZNkUAghhBBCCCHikKwZFEIIIYQQQog4JMmgEEIIIYQQQsQhSQaFEEIIIYQQIg5JMiiEEEIIIYQQcUiSQSGEEEIIIYSIQ/8faYbaS7OXyuMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# coding: utf-8\n",
    "\n",
    "\n",
    "import scipy.io as sio    # 用于导入mat文件\n",
    "import seaborn as sns     # 用于绘制散点图\n",
    "import scipy.stats as stats   # 用于绘制高斯分布图\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "sns.set(style=\"white\")\n",
    "plt.style.use({'figure.figsize':(15, 8)})\n",
    "#sns.set()\n",
    "\n",
    "def gaussian2d(mean, cov):\n",
    "    # 绘制2维高斯分布的轮廓线\n",
    "    # 输入：mean:期望；cov：协方差矩阵\n",
    "    # 输出：轮廓线上的坐标\n",
    "    n = 200        # 绘图需要的点的数量\n",
    "    eigvals, U = np.linalg.eig(cov)   # 矩阵U表示由特征向量组成的正交矩阵\n",
    "    D = np.diag(eigvals)  # 由特征值构造出来的对角矩阵D\n",
    "    k = np.power(stats.chi2.ppf(0.95, 2), 0.5)    # 绘制出95%概率质量覆盖的轮廓线，此处使用了卡方分布的相关知识\n",
    "    t = np.linspace(0, 2*np.pi, n)\n",
    "    xy = np.array([np.cos(t), np.sin(t)])\n",
    "    w = np.dot(np.dot(k*U,np. power(D,0.5)), xy)\n",
    "    z = w + mean[:,np.newaxis]        # z为%95概率质量的轮廓线上的坐标          \n",
    "    return z\n",
    "\n",
    "def kindScatter(x_label, y_label, group_label, ax, data, size=100, legendloc=2):\n",
    "    \"\"\"实现对类别数据的绘图，根据其类别进行绘图\n",
    "    参数： x_label:x坐标的特征名;y_label:y坐标的特征名；group_label:类别所在的特征名\n",
    "           ax:绘图所基于的坐标轴;data:pandas数据类型，绘图所需的数据      \n",
    "    \"\"\"\n",
    "    kind = np.unique(data[group_label])\n",
    "    if len(kind) > 7:\n",
    "        print(\"类别不允许超过7个\")\n",
    "    else:\n",
    "        markers = ['o', '*', '+', 'x','s', 'p','h']\n",
    "        col = ['b','r','g','c','y','m','k']\n",
    "        for i in range(len(kind)):\n",
    "            xx = data[x_label][data[group_label] == kind[i]]\n",
    "            yy = data[y_label][data[group_label] == kind[i]]      \n",
    "            ax.scatter(xx, yy, marker=markers[i], s=size, c=col[i],\n",
    "            alpha=0.8, label=kind[i])\n",
    "            plt.legend(loc=legendloc, frameon=True)\n",
    "\n",
    "\n",
    "filename = 'heightWeight.mat'\n",
    "raw_data = sio.loadmat(filename)\n",
    "raw_data.keys()  # 查看数据的键\n",
    "print(type(raw_data['heightWeightData']))\n",
    "# 对数据进行初步了解\n",
    "data = raw_data['heightWeightData']\n",
    "print(data[:5,:])       # 打印数据前5行，第1列：1表示男性，2代表女性；第2列：体重；第3列：身高\n",
    "\n",
    "# 将numpy数组转换为pandas数据结构，以使用seaborn绘图\n",
    "columns = ['sex', 'height', 'Weight']\n",
    "df = pd.DataFrame(data=data, columns=columns)\n",
    "df['sex']=df['sex'].astype('category')    # 将sex这一列转换为类别类数据\n",
    "df['sex'].cat.categories=['male', 'female']  # 将类别1,2转换为相应的类别\n",
    "# sns.relplot(x='height', y='Weight', hue='sex', style='sex', data=df)   # 利用seaborn进行绘图\n",
    "ax1=plt.subplot(121)\n",
    "kindScatter(x_label='height', y_label='Weight', group_label='sex', ax=ax1, data=df)\n",
    "ax1.set_xlabel('height')\n",
    "ax1.set_ylabel('Weight')\n",
    "\n",
    "\n",
    "# 将属于男性的数据提取出来\n",
    "df_male = df[df['sex']=='male']\n",
    "# 将属于女性的数据提取出来\n",
    "df_female = df[df['sex']=='female']\n",
    "# 对两组数据进行统计，获取高斯分布的期望和协方差矩阵的MLE\n",
    "cov = df_male.cov(),df_female.cov()  \n",
    "mean = df_male.mean(),df_female.mean()\n",
    "\n",
    "ax2 = plt.subplot(122)\n",
    "kindScatter(x_label='height', y_label='Weight', group_label='sex', ax=ax2, data=df)\n",
    "col = ['b','r','g','c','y','m','k']\n",
    "for i in range(len(cov)):\n",
    "    z = gaussian2d(mean[i],cov[i])\n",
    "    ax2.plot(z[0,:],z[1,:],color=col[i])\n",
    "ax2.set_xlabel('height')\n",
    "ax2.set_ylabel('Weight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "diag = np.diag([1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 0],\n",
       "        [0, 1]],\n",
       "\n",
       "       [[1, 0],\n",
       "        [0, 1]]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile(diag,[2,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "cov1 = np.array([[1.5,0],[0,1]])\n",
    "cov2 = 0.7*np.diag([1,1])\n",
    "cov = np.vstack((cov1,cov2)).reshape(2,2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.5, 0. ],\n",
       "        [0. , 1. ]],\n",
       "\n",
       "       [[0.7, 0. ],\n",
       "        [0. , 0.7]]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.8])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(np.array([0.8,0.2]))[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False, False,  True, False,  True, False,  True,\n",
       "       False, False,  True, False, False, False, False, False, False,\n",
       "       False, False])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prob=[0.8,0.2]\n",
    "n_samples=20\n",
    "R = np.random.randn(n_samples)\n",
    "M = np.zeros(n_samples)\n",
    "cumprob = np.cumsum(prob)\n",
    "R2 = np.array(R>cumprob[0])\n",
    "R2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0.])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 0., 0., 0., 1., 0., 1., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0.])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R2+M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.19995242e+00, -7.34606623e-01,  7.25169066e-02, -5.85700926e-01,\n",
       "        2.36986791e+00, -6.99513079e-01,  9.21832392e-01, -7.02834167e-01,\n",
       "        1.19068753e+00, -2.56281106e-01,  2.78296256e-02,  1.03964022e+00,\n",
       "        8.43385083e-04,  3.57401406e-01,  3.87323370e-01, -5.64669598e-01,\n",
       "       -3.95764355e-01, -3.04254467e-02,  8.29178868e-02, -5.26959622e-01])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'scipy.stats' from 'c:\\\\users\\\\administrator\\\\appdata\\\\local\\\\programs\\\\python\\\\python35\\\\lib\\\\site-packages\\\\scipy\\\\stats\\\\__init__.py'>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.multivariate_normal.rvs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.718281828459045"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(np.e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 2],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[3,2],[2,3]])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object `np.chol` not found.\n"
     ]
    }
   ],
   "source": [
    "np.chol?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.73205081, 0.        ],\n",
       "       [1.15470054, 1.29099445]])"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "di = np.linalg.cholesky(x)\n",
    "di"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.73205081, 1.29099445])"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(di)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[-1.  , -1.  ],\n",
       "        [-1.  , -0.99],\n",
       "        [-1.  , -0.98],\n",
       "        ...,\n",
       "        [-1.  ,  0.97],\n",
       "        [-1.  ,  0.98],\n",
       "        [-1.  ,  0.99]],\n",
       "\n",
       "       [[-0.99, -1.  ],\n",
       "        [-0.99, -0.99],\n",
       "        [-0.99, -0.98],\n",
       "        ...,\n",
       "        [-0.99,  0.97],\n",
       "        [-0.99,  0.98],\n",
       "        [-0.99,  0.99]],\n",
       "\n",
       "       [[-0.98, -1.  ],\n",
       "        [-0.98, -0.99],\n",
       "        [-0.98, -0.98],\n",
       "        ...,\n",
       "        [-0.98,  0.97],\n",
       "        [-0.98,  0.98],\n",
       "        [-0.98,  0.99]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 0.97, -1.  ],\n",
       "        [ 0.97, -0.99],\n",
       "        [ 0.97, -0.98],\n",
       "        ...,\n",
       "        [ 0.97,  0.97],\n",
       "        [ 0.97,  0.98],\n",
       "        [ 0.97,  0.99]],\n",
       "\n",
       "       [[ 0.98, -1.  ],\n",
       "        [ 0.98, -0.99],\n",
       "        [ 0.98, -0.98],\n",
       "        ...,\n",
       "        [ 0.98,  0.97],\n",
       "        [ 0.98,  0.98],\n",
       "        [ 0.98,  0.99]],\n",
       "\n",
       "       [[ 0.99, -1.  ],\n",
       "        [ 0.99, -0.99],\n",
       "        [ 0.99, -0.98],\n",
       "        ...,\n",
       "        [ 0.99,  0.97],\n",
       "        [ 0.99,  0.98],\n",
       "        [ 0.99,  0.99]]])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x, y = np.mgrid[-1:1:.01, -1:1:.01]\n",
    "pos = np.dstack((x, y))\n",
    "pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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